Supply chain management
chen11111
Paul A. Souders/Corbis
Chapter
eleven
Chapter Outline
Introduction
11.1 The Role of Inventory
11.2 Periodic Review Systems
11.3 Continuous Review Systems
11.4 Single-Period Inventory Systems
11.5 Inventory in the Supply Chain Chapter Summary
Managing Inventory throughout the Supply Chain
Chapter ObjeCtives
By the end of this chapter, you will be able to:
· Describe the various roles of inventory, including the different types of inventory and inventory drivers, and distinguish between independent demand and dependent demand inventory.
· Calculate the restocking level for a periodic review system.
· Calculate the economic order quantity (EOQ) and reorder point (ROP) for a continuous review system, and determine the best order quantity when volume discounts are available.
· Calculate the target service level and target stocking point for a single-period inventory system.
· Describe how inventory decisions affect other areas of the supply chain. In particular, describe the bullwhip effect, inventory positioning issues, and the impacts of transportation, packaging, and material handling considerations.
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CHAPTER 11 • Managing Inventory throughout the Supply Chain 327
Inventory Management at Amazon.com
Baumgarten/VARIO IMAGES/SIPA/Newscom |
Employees pick items off the shelves at an Amazon.com warehouse in Leipzig, Germany.
WHEN they first started appearing in the late 1990s, Web- based “e-tailers” such as Amazon.com hoped to replace the “bricks” of traditional retailing with the
“clicks” of online ordering. Rather than opening dozens or even hundreds of stores filled with expensive inventory, an e-tailer could run a single virtual store that served cus-tomers around the globe. Their business model suggested that inventory could be kept at a few key sites, chosen to minimize costs and facilitate quick delivery to custom-ers. In theory, e-tailers were highly “scalable” businesses that could add new customers with little or no additional investment in inventory or facilities. (Traditional retailers usually need to add stores to gain significant increases in their customer base.)
But how has this actually played out for Amazon over the years? Table 11.1 contains sales and inventory figures, pulled from the company’s annual reports, for Amazon for the years 1997 through 2012. The first column reports net sales for each calendar year, and the second column contains the amount of inventory on hand at the end of the year. The third column shows inventory turns, which is calculated as (net sales/ending inventory). Retailers generally want higher inventory turns, which indicate that they can support the same level of sales with less inventory. Inventory turns is of-ten thought of as a key measure of asset productivity.
Looking at Amazon’s performance over the years provides some interesting insights. Consider Figure 11.1. In late 1999, Amazon learned that managing inventory can be challenging even for e-tailers. That was the year the com-pany expanded into new product lines, such as electron-ics and housewares, with which it had little experience.
Table 11.1 Amazon.com Financial Results, 1997–2012
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|
Inventory |
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|
Net Sales |
($Millions) |
Inventory |
Year |
($Millions) |
(Dec. 31) |
Turns |
1997 |
$148 |
$9 |
16.4 |
1998 |
$610 |
$30 |
20.3 |
1999 |
$1,640 |
$221 |
7.4 |
2000 |
$2,762 |
$175 |
15.8 |
2001 |
$3,122 |
$143 |
21.8 |
2002 |
$3,933 |
$202 |
19.5 |
2003 |
$5,264 |
$294 |
17.9 |
2004 |
$6,921 |
$480 |
14.4 |
2005 |
$8,490 |
$566 |
15.0 |
2006 |
$10,711 |
$877 |
12.2 |
2007 |
$14,835 |
$1,200 |
12.4 |
2008 |
$19,166 |
$1,399 |
13.7 |
2009 |
$24,509 |
$2,171 |
11.3 |
2010 |
$34,204 |
$3,202 |
10.7 |
2011 |
$48,077 |
$4,992 |
9.6 |
2012 |
$61,093 |
$6,031 |
10.1 |
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|
Amazon’s purchasing managers were faced with the ques-tion of how many of these items to hold in inventory. Too little, and they risked losing orders and alienating custom-ers; too much, and they could lock up the company’s re-sources in unsold products. Only later, when sales for the 1999 holiday season fell flat and Amazon’s inventory levels skyrocketed did the purchasing managers realize they had overstocked. In fact, as the figures show, by the end of 1999,
328 PART IV • Planning and Controlling Operations and Supply Chains
Inventory Turns at Amazon.com, 1997–2009
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Figure 11.1 Inventory Turns at Amazon.com, 1997–2009
Amazon’s inventory turnover ratio was 7.4—worse than that of the typical brick-and-mortar retailer at the time.
After 1999, Amazon seemed to learn its lesson. Inven-tory turns rose to nearly 22 in 2001, but have fallen steadily ever since, to 10.1 turns for 2012, even as Amazon’s sales have risen sharply. But why? The decline in inventory turns over the past decade is due in large part to a shift in Amazon’s business strategy. Instead of trying to build com-petitive advantage based on low-cost books (Amazon’s original business model), the company now seeks to provide
customers with convenient shopping and fast delivery for a wide range of products. Such a strategy requires more in-ventory to support the same level of sales.
So today, how does Amazon compare to its brick-and-mortar competitors? Amazon handily beats traditional book retailer Barnes & Noble, whose inventory turns for 2013 were just 4.6. Yet Best Buy, which sells computers, phones, video games, and appliances, generated 6.9 inventory turns in 2013—not bad, especially considering all the retail stores Best Buy must support.
Inventory
According to APICS, “those stocks or items used to sup-port production (raw materials and work-in-process items), supporting activities (mainte-nance, repair, and operating supplies) and customer service (finished goods and spare parts).”
APICS defines inventory as “those stocks or items used to support production (raw materials and work-in-process items), supporting activities (maintenance, repair, and operating supplies) and customer service (finished goods and spare parts) .”1 In this chapter, we discuss the critical role of inventory—why it is necessary, what purposes it serves, and how it is controlled.
As Amazon’s experience suggests, inventory management is still an important function, even in the Internet age. In fact, many managers seem to have a love–hate relationship with inventory. Michael Dell talks about inventory velocity—the speed at which components move through Dell Computer’s operations—as a key measure of his company’s performance.2 In his mind, the less inventory the company has sitting in the warehouse, the better. Victor Fung of the Hong Kong-based trading firm Li & Fung, goes so far as to say, “Inventory is the root of all evil.”3
Yet look what happened to the price of gasoline in the United States during the spring of 2007. It skyrocketed, primarily because refineries were shut down for maintenance and suppliers were caught with inadequate reserves. And if you have ever visited a store only to find that your favorite product is sold out, you might think the lack of inventory is the root of all evil. The fact is, inventory is both a valuable resource and a potential source of waste.
1Definition of Inventory in J. H. Blackstone, ed., APICS Dictionary, 14th ed. (Chicago, IL: APICS, 2013). Reprinted by
permission.
2J. Magretta, “The Power of Virtual Integration: An Interview with Dell Computer’s Michael Dell,” Harvard Business
Review 76, no. 2 (March–April 1998): 72–84.
3J. Magretta, “Fast, Global, and Entrepreneurial: Supply Chain Management, Hong Kong Style,” Harvard Business Review
76, no. 5 (September–October 1998): 102–109.
CHAPTER 11 • Managing Inventory throughout the Supply Chain 329
11.1 The Role of Inventory
Consider WolfByte Computers, a fictional manufacturer of laptops, tablets and e-readers. Fig- HYPERLINK \l "page346" ure 11.2 shows the supply chain for WolfByte’s laptop computers. WolfByte assembles the laptops from components purchased from companies throughout the world, three of which are shown in the figure. Supplier 1 provides the displays, Supplier 2 manufactures the hard drives, and Sup-plier 3 produces the keyboards.
Looking downstream, WolfByte sells its products through independent retail stores and through its own Web site. At retail stores, customers can buy a laptop off the shelf, or they can order one to be customized and shipped directly to them. On average, WolfByte takes about two days to ship a computer from its assembly plant to a retail store or a customer. Both WolfByte and the retail stores keep spare parts on hand to handle customers’ warranty claims and other service requirements.
With this background, let’s discuss the basic types of inventory and see how they fit into WolfByte’s supply chain.
Cycle stock
Components or products that are received in bulk by a downstream partner, gradually used up, and then replenished again in bulk by the upstream partner.
Safety stock
Extra inventory that a company holds to protect itself against uncertainties in either demand or replenishment time.
Two of the most common types of inventory are cycle stock and safety stock. Cycle stock refers to components or products that are received in bulk by a downstream partner, gradually used up, and then replenished again in bulk by the upstream partner. For example, suppose Supplier 3 ships 20,000 keyboards at a time to WolfByte. Of course, WolfByte can’t use all those devices at once. More likely, workers pull them out of inventory as needed. Eventually, the inventory runs down, and WolfByte places another order for keyboards. When the new order arrives, the inven-tory level rises and the cycle is repeated. Figure 11.3 shows the classic sawtooth pattern associ-ated with cycle stock inventories.
Cycle stock exists at other points in WolfByte’s supply chain. Almost certainly, Suppliers 1 through 3 have cycle stocks of raw materials that they use to make components. And retailers need to keep cycle stocks of both completed computers and spare parts in order to serve their customers.
Cycle stock is often thought of as active inventory because companies are constantly using it up, and their suppliers constantly replenishing it. Safety stock, on the other hand, is extra in-ventory that companies hold to protect themselves against uncertainties in either demand levels or replenishment time. Companies do not plan on using their safety stock any more than you plan on using the spare tire in the trunk of your car; it is there just in case.
Let’s return to the keyboard example in Figure 11.3. WolfByte has timed its orders so that a new batch of keyboards comes in just as the old batch is used up. But what if Supplier 3 is late in delivering the devices? What if demand is higher than expected? If either or both these condi-tions occur, WolfByte could run out of keyboards before the next order arrives.
Imagine the resulting chaos: Assembly lines would have to shut down, customers’ orders couldn’t be filled, and WolfByte would have to notify customers, retailers, and shippers about the delays.
Figure 11.2
WolfByte Computers
Supply Chain
Supplier 1
WolfByte
Computers
Supplier 2
Supplier 3
Customer Retail store
Customer
330 PART IV • Planning and Controlling Operations and Supply Chains
Figure 11.3
Cycle Stock at WolfByte
Computers
Anticipation inventory
Inventory that is held in antici-pation of customer demand.
Hedge inventory
According to APICS, a “form of inventory buildup to buffer against some event that may not happen. Hedge inventory planning involves specula-tion related to potential labor strikes, price increases, unset-tled governments, and events that could severely impair the company’s strategic initiatives.”
Transportation inventory
Inventory that is moving from one link in the supply chain to another.
Smoothing inventory
Inventory that is used to smooth out differences between upstream produc-tion levels and downstream demand.
Figure 11.4
Safety Stock at WolfByte
Computers
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Another order |
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received ... |
received ... |
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One solution is to hold some extra inventory, or safety stock, of keyboards to protect against fluctuations in demand or replenishment time. Figure 11.4 shows what WolfByte’s inventory levels would look like if the company decided to hold safety stock of 1,000 keyboards. As you can see, safety stock provides valuable protection, but at the cost of higher inventory lev-els. Later in the chapter, we discuss ways of calculating appropriate safety stock levels.
There are four other common types of inventory: anticipation, hedge, transportation, and smoothing. Anticipation inventory, as the name implies, is inventory that is held in anticipation of customer demand. Anticipation inventory allows instant availability of items when custom-ers want them. Hedge inventory, according to APICS, is “a form of inventory buildup to buffer against some event that may not happen. Hedge inventory planning involves speculation related to potential labor strikes, price increases, unsettled governments, and events that could severely impair the company’s strategic initiatives.”4 In this sense, hedge inventories can be thought of as a special form of safety stock. WolfByte has stockpiled a hedge inventory of two months’ worth of hard drives because managers have heard that Supplier 2 may experience a temporary shut-down over the next two months.
Transportation inventory represents inventory that is “in the pipeline,” moving from one link in the supply chain to another. When the physical distance between supply chain partners is long, transportation inventory can represent a considerable investment. Suppose, for example, that Supplier 2 is located in South Korea, and WolfByte is located in Texas. Hard drives may take several weeks to travel the entire distance between the two companies. As a result, multiple orders could be in the pipeline on any particular day. One shipment of hard drives might be sitting on the docks in Kimhae, South Korea; two others might be halfway across the Pacific; a fourth might be found on Route I-10, just outside Phoenix, Arizona. In fact, the transportation inventory of hard drives alone might dwarf the total cycle and safety stock inventories in the rest of the supply chain.
Finally, smoothing inventory is used to smooth out differences between upstream pro-duction levels and downstream demand. Suppose management has determined that WolfByte’s assembly plant is most productive when it produces 3,000 laptops a day. Unfortunately, demand from retailers and customers will almost certainly vary from day to day. As a result, WolfByte’s
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Another |
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received ... |
order received ... |
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11,000 |
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And the |
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repeats itself |
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4Definition of Hedge Inventory in J. H. Blackstone, ed., APICS Dictionary, 14th ed. (Chicago, IL: APICS, 2013). Reprinted by permission.
Figure 11.5
Smoothing Inventories at
WolfByte Computers
CHAPTER 11 • Managing Inventory throughout the Supply Chain 331
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managers may decide to produce a constant 3,000 laptops per day, building up finished goods inventory during periods of slow demand and drawing it down during periods of high demand. (Figure 11.5 illustrates this approach.) Smoothing inventories allow individual links in the sup-ply chain to stabilize their production at the most efficient level and to avoid the costs and head-aches associated with constantly changing workforce levels and/or production rates. If you think you may have heard of this idea before, you have: It’s part of the rationale for following a level production strategy in developing a sales and operations plan (see Chapter 10).
Inventory drivers
Business conditions that force companies to hold inventory.
Supply uncertainty
The risk of interruptions in the flow of components from upstream suppliers.
From this discussion, we can see that inventory is a useful resource. However, companies don’t want to hold more inventory than necessary. Inventory ties up space and capital: A dollar invested in inventory is a dollar that cannot be used somewhere else. Likewise, the space used to store inventory can often be put to more productive use. Inventory also poses a significant risk of obsolescence, particularly in supply chains with short product life cycles. Consider what happens when Intel announces the next generation of processor chips. Would you want to be stuck hold-ing the old-generation chips when the new ones hit the market?
Finally, inventory is too often used to hide problems that management really should resolve. In this sense, inventory can serve as a kind of painkiller, treating the symptom without solving the underlying problem. Consider our discussion of safety stock. Suppose WolfByte’s managers decide to hold additional safety stock of hard drives because of quality problems they have been experi-encing with units received from Supplier 2. While the safety stock may buffer WolfByte from these quality problems, it does so at a cost. A better solution might be to improve the quality of incoming units, thereby reducing both quality-related costs and the need for additional safety stock.
With these concerns in mind, let’s turn our attention to inventory drivers—business condi-tions that force companies to hold inventory. Table 11.2 summarizes the ways in which various inventory drivers affect different types of inventory. To the extent that organizations can manage and control the drivers of inventories, they can reduce the supply chain’s need for inventory.
In managing inventory, organizations face uncertainty throughout the supply chain. On the upstream (supplier) end, they face supply uncertainty, or the risk of interruptions in the
Table 11.2
Inventory Drivers and
Their Impact
Inventory Driver |
Impact |
Uncertainty in supply or demand |
Safety stock, hedge inventory |
Mismatch between a downstream partner’s demand and the most |
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efficient production or shipment volumes for an upstream partner |
Cycle stock |
Mismatch between downstream demand levels and upstream |
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production capacity |
Smoothing inventory |
Mismatch between timing of customer demand and supply |
Anticipation inventory |
chain lead times |
Transportation inventory |
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332 PART IV • Planning and Controlling Operations and Supply Chains
Demand uncertainty
The risk of significant and unpredictable fluctuations in downstream demand.
flow of components they need for their internal operations. In assessing supply uncertainty, managers need to answer questions such as these:
· How consistent is the quality of the goods being purchased?
· How reliable are the supplier’s delivery estimates?
· Are the goods subject to unexpected price increases or shortages?
Problems in any of these areas can drive up supply uncertainty, forcing an organization to hold safety stock or hedging inventories.
On the downstream (customer) side, organizations face demand uncertainty, or the risk of significant and unpredictable fluctuations in the demand for their products. For example, many suppliers of automobile components complain that the big automobile manufacturers’ forecasts are unreliable and that order sizes are always changing, often at the last minute. Under such conditions, suppliers are forced to hold extra safety stock to meet unexpected jumps in de-mand or changes in order size.
In dealing with uncertainty in supply and demand, the trick is to determine what types of uncertainty can be reduced and then to focus on reducing them. For example, poor quality is a source of supply uncertainty that can be substantially reduced or even eliminated through business process or quality improvement programs, such as those we discussed in Chapters 4 and 5. On the other hand, forecasting may help to reduce demand uncertainty, but it can never completely eliminate it.
Another common inventory driver is the mismatch between demand and the most efficient production or shipment volumes. Let’s start with a simple example—facial tissue. When you blow your nose, how many tissues do you use? Most people would say 1, yet tissues typically come in boxes of 200 or more. Clearly, a mismatch exists between the number of tissues you need at any one time and the number you need to purchase. The reason, of course, is that packaging, shipping, and selling facial tissues one at a time would be highly inefficient, especially because the cost of holding a cycle stock of facial tissues is trivial. On an organizational scale, mismatches between demand and efficient production or shipment volumes are the main drivers of cycle stocks. As we will see later in this chapter, managers can often alter their business processes to reduce produc-tion or shipment volumes, thereby reducing the mismatch with demand and the resulting need for cycle stocks.
Likewise, mismatches between overall demand levels and production capacity can force companies to hold smoothing inventories (Figure 11.5). Of course, managers can reduce smooth-ing inventories by varying their capacity to better match demand or by smoothing demand to better match capacity. As we saw in Chapter 10, both strategies have pros and cons.
The last inventory driver we will discuss is a mismatch between the timing of the cus-tomer’s demand and the supply chain’s lead time. When you go to the grocery store, you expect to find fresh produce ready to buy; your expected waiting time is zero. But produce can come from almost anywhere in the world, depending on the season. To make sure that bananas and lettuce will be ready and waiting for you at your local store, someone has to initiate their move-ment through the supply chain days or even weeks ahead of time and determine how much anticipation inventory to hold. Whenever the customer’s maximum waiting time is shorter than the supply chain’s lead time, companies must have transportation and anticipation inventories to ensure that the product will be available when the customer wants it.
How can businesses reduce the need to hold anticipation inventory? Often they do so both by shrinking their own lead time and by persuading customers to wait longer. It’s hard to be-lieve now, but personal computers once took many weeks to work their way through the supply chain. As a result, manufacturers were forced to hold anticipation inventories to meet customer demand. Today, manufacturers assemble and ship a customized laptop or tablet directly to the customer’s front door in just a few days. Customers get fast and convenient delivery of a prod-uct that meets their exact needs. At the same time, the manufacturer can greatly reduce or even eliminate anticipation inventory.
In the remainder of this chapter, we examine the systems that are used in managing vari-ous types of inventory. Before beginning a detailed discussion of these tools and techniques of inventory management, however, we need to distinguish between two basic inventory catego-ries: independent demand and dependent demand inventory. The distinction between the two is crucial because the tools and techniques needed to manage each are very different.
Independent demand inventory
Inventory items whose demand levels are beyond a company’s complete control.
Dependent demand inventory
Inventory items whose demand levels are tied directly to a company’s planned production of another item.
CHAPTER 11 • Managing Inventory throughout the Supply Chain 333
Independent versus Dependent Demand Inventory
In general, independent demand inventory refers to inventory items whose demand levels are beyond a company’s complete control. Dependent demand inventory, on the other hand, refers to inventory items whose demand levels are tied directly to the company’s planned production of another item. Because the required quantities and timing of dependent demand inventory items can be predicted with great accuracy, they are under a company’s complete control.
A simple example of an independent demand inventory item is a kitchen table. While a furniture manufacturer may use forecasting models to predict the demand for kitchen tables and may try to use pricing and promotions to manipulate demand, the actual demand for kitchen tables is unpredictable. The fact is that customers determine the demand for these items, so fin-ished tables clearly fit the definition of independent demand inventory.
But what about the components that are used to make the tables, such as legs? Suppose that a manufacturer has decided to produce 500 tables five weeks from now. With this informa-tion, a manager can quickly calculate exactly how many legs will be needed:
500 * 4 legs per table = 2,000 legs
Furthermore, the manager can determine exactly when the legs will be needed, based on the company’s production schedule. Because the timing and quantity of the demand for table legs are completely predictable and under the manager’s total control, the legs fit the definition of dependent demand items. Dependent demand items require an entirely different approach to managing than do independent demand items. We discuss ways of managing dependent demand items in more depth in Chapter 12.
Three basic approaches are used to manage independent demand inventory items: periodic review systems, continuous review systems, and single-period inventory systems. We examine all three approaches in the following sections.
Periodic review system
An inventory system that is used to manage indepen-dent demand inventory. The inventory level for an item is checked at regular intervals and restocked to some prede-termined level.
One of the simplest approaches to managing independent demand inventory is based on a periodic review of inventory levels. In a periodic review system, a company checks the inven-tory level of an item at regular intervals and restocks to some predetermined level, R. The actual order quantity, Q, is the amount required to bring the inventory level back up to R. Stated more formally:
Q = R - I |
(11.1) |
where:
Q = order quantity
R = restocking level
I = inventory level at the time of review
Figure 11.6 shows the fluctuations in the inventory levels of a single item under a two-week periodic review system. As the downward-sloping line shows, the inventory starts out full and then slowly drains down as units are pulled from it. (Note that the line will be straight only if demand is constant.) After two weeks, the inventory is replenished, and the process begins again.
Figure 11.6
Periodic Review System
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334 PART IV • Planning and Controlling Operations and Supply Chains
A periodic review system nicely illustrates the use of both cycle stock and safety stock. By replenishing inventory every two weeks, rather than daily or even hourly, the organization spreads the cyclical cost of restocking across more units. And the need to hold safety stock helps to determine the restocking level. Increasing the restocking level effectively increases safety stock: The higher the level, the less likely the organization is to run out of inventory before the next replenishment period. On the flip side, because inventory is checked only at regular inter-vals, the company could run out of an item before the inventory is replenished. In fact, that is exactly what happens just before week 6 in Figure 11.6. If you have ever visited your favorite vending machine, only to find that the item you wanted has been sold out, you have been the victim of a periodic review system stockout.
As you might imagine, a periodic review system is best suited to items for which periodic restocking is economical and the cost of a high restocking level (and hence a large safety stock) is not prohibitive. A classic example is a snack food display at a grocery store. Constantly moni-toring inventory levels for low-value items such as pretzels or potato chips makes no economic sense. Rather, a vendor will stop by a store regularly and top off the supply of all the items, usu-ally with more than enough to meet demand until the next replenishment date.
Service level
A term used to indicate the amount of demand to be met under conditions of demand and supply uncertainty.
The key question in setting up a periodic review system is determining the restocking level, R.
In general, R should be high enough to meet all but the most extreme demand levels during the reorder period (RP) and the time it takes for the order to come in (L). Specifically:
R = mRP + L + zsRP + L |
(11.2) |
where:
mRP + L = average demand during the reorder period and the order lead time
sRP + L = standard deviation of demand during the reorder period and the order lead time
· number of standard deviations above the average demand (higher z values increase the restocking level, thereby lowering the probability of a stockout)
Equation (11.2) assumes that the demand during the reorder period and the order lead time is normally distributed. By setting R a certain number of standard deviations above the average, a firm can establish a service level, which indicates what percentage of the time inven-tory levels will be high enough to meet demand during the reorder period. For example, setting z = 1.28 would make R large enough to meet expected demand 90% of the time (i.e., provide a 90% service level), while setting z = 2.33 would provide a 99% service level. Different z values and the resulting service levels are listed in the following table. (More values can be derived from the normal curves area table in Appendix I.)
z Value |
Resulting Service Level |
1.28 |
90% |
1.65 |
95 |
2.33 |
99 |
3.08 |
99.9 |
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EXAMPLE 11.1
Establishing a Periodic
Review System for
McCreery’s Chips
McCreery’s Chips sells large tins of potato chips at a grocery superstore. Every 10 days, a McCreery’s deliveryperson stops by and checks the inventory level. He then places an order, which is delivered three days later. Average demand during the reorder period and order lead time (13 days total) is 240 tins. The standard deviation of demand during this same time period is 40 tins. The grocery superstore wants enough inventory on hand to meet demand 95% of the time. In other words, the store is willing to take a 5% chance that it will run out of tins before the next order arrives.
CHAPTER 11 • Managing Inventory throughout the Supply Chain 335
Using this information, McCreery’s establishes the following restocking level:
R = mRP + L + zsRP + L
= 240 tins + 1.65*40 tins = 306 tins
Suppose the next time the deliveryperson stops by, he counts 45 tins. Based on this information, he will order Q = 306 - 45 = 261 tins, which will be delivered in three days.
11.3 Continuous Review Systems
Continuous review system
An inventory system used to manage independent demand inventory. The inventory
level for an item is constantly monitored, and when the reorder point is reached, an order is released.
While the periodic review system is straightforward, it is not well suited to managing critical and/or expensive inventory items. A more sophisticated approach is needed for these types of in-ventory. In a continuous review system, the inventory level for an item is constantly monitored, and when the reorder point is reached, an order is released.
A continuous review system has several key features:
1. Inventory levels are monitored constantly, and a replenishment order is issued only when a preestablished reorder point has been reached.
2. The size of a replenishment order is typically based on the trade-off between holding costs and ordering costs.
3. The reorder point is based on both demand and supply considerations, as well as on how much safety stock managers want to hold.
To simplify our discussion of continuous review systems, we will begin by assuming that the variables that underlie the system are constant. Specifically:
1. The inventory item we are interested in has a constant demand per period, d. That is, there is no variability in demand from one period to the next. Demand for the year is D.
2. L is the lead time, or number of periods that must pass before a replenishment order ar-rives. L is also constant.
3. H is the cost of holding a single unit in inventory for a year. It includes the cost of the space needed to store the unit, the cost of potential obsolescence, and the opportunity cost of tying up the organization’s funds in inventory. H is known and fixed.
4. S is the cost of placing an order, regardless of the order quantity. For example, the cost to place an order might be $100, whether the order is for 2 or 2,000 units. S is also known and fixed.
5. P, the price of each unit, is fixed.
Under these assumptions, the fluctuations in the inventory levels for an item will look like those in Figure 11.7. Inventory levels start out at Q, the order quantity, and decrease at a constant rate, d. Because this is a continuous review system, the next order is issued when the reorder point, labeled ROP, is reached. What should the reorder point be? In this simple model, in which the demand rate and lead time are constant, we should reorder when the inventory level reaches the point where there are just enough units left to meet requirements until the next order arrives:
ROP = dL |
(11.3) |
Figure 11.7
Continuous Review System
(with Constant Demand
Rate d)
For example, if the demand rate is 50 units a week and the lead time is 3 weeks, the manager should place an order when the inventory level drops to 150 units. If everything goes according
level |
Q |
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Slope = –d |
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Inventory |
ROP |
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Time |
336 PART IV • Planning and Controlling Operations and Supply Chains
Figure 11.8
The Effect of Halving the
Order Quantity
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Q |
level |
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Inventory |
Q' |
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ROP |
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Time |
Economic order quantity (EOQ)
The order quantity that minimizes annual holding and ordering costs for an item.
to plan, the firm will run out of units just as the next order arrives. Finally, because the inventory
Q level in this model goes from Q to 0 over and over again, the average inventory level is 2 .
The Economic Order Quantity (EOQ)
How do managers of a continuous review system choose the order quantity (Q)? Is there a “best” order quantity, and if so, how do holding costs (H) and ordering costs (S) affect it? To understand the role of holding and ordering costs in a continuous review system, let’s see what happens if the order quantity is sliced in half, to Q as shown in Figure 11.8. The result: With quantity Q the manager ends up ordering twice as often, which doubles the company’s ordering costs. On the other hand, cutting the order quantity in half also halves the average inventory level, which low-ers holding costs.
The relationship between holding costs and ordering costs can be seen in the following equation:
Total holding and ordering cost for the year = total yearly holding cost |
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+ total yearly ordering cost |
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Q |
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= a |
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bH + |
a |
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(11.4) |
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Q |
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Yearly holding cost is calculated by taking the average inventory level (Q/2) and multiply-ing it by the per-unit holding cost. Yearly ordering cost is calculated by calculating the number of times we order per year (D/Q) and multiplying this by the fixed ordering cost.
As Equation (11.4) suggests, there is a trade-off between yearly holding costs and ordering costs. Reducing the order quantity, Q, will decrease holding costs, but force the organization to order more often. Conversely, increasing Q will reduce the number of times an order must be placed, but result in higher average inventory levels.
Figure 11.9 shows graphically how yearly holding and ordering costs react as the order quantity, Q, varies. In addition to showing the cost curves for yearly holding costs and yearly ordering costs, Figure 11.9 includes a total cost curve that combines these two. If you look closely, you can see that the lowest point on the total cost curve also happens to be where yearly holding costs equal yearly ordering costs.
Figure 11.9 illustrates the economic order quantity (EOQ) , the particular order quantity (Q) that minimizes holding costs and ordering costs for an item. This special order quantity is found by setting yearly holding costs equal to yearly ordering costs and solving for Q:
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(11.5) |
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H |
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where:
Q = order quantity
H = annual holding cost per unit D = annual demand
S = ordering cost
Figure 11.9
The Relationships among Yearly Holding Costs, Yearly Ordering Costs, and the Order Quantity, Q
EXAMPLE 11.2
Calculating the EOQ at
Boyer’s Department
Store
CHAPTER 11 • Managing Inventory throughout the Supply Chain 337
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(H |
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(QD |
(S |
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Order quantity (Q)
As Figure 11.9 shows, order quantities that are higher than the EOQ will result in annual holding costs that are higher than ordering costs. Conversely, order quantities that are lower than the EOQ will result in annual ordering costs that are higher than holding costs.
You are in charge of ordering items for Boyer’s Department Store, located in Seattle. For one of the products Boyer’s carries, the Hudson Valley Model Y ceiling fan, you have the following information:
Annual demand (D) = 4,000 fans a year
Annual holding cost (H) = +15 per fan
Ordering cost (S) = +50 per order
Your predecessor ordered fans four times a year, in quantities (Q) of 1,000. The result-ing annual holding and ordering costs were:
Holding costs for the year + ordering costs for the year
· (1,000 2)+15 + (4,000 1,000)+50
· +7,500 + +200 = +7,700
Because holding costs are much higher than ordering costs, we know that the EOQ must be much lower than 1,000 fans. In fact:
EOQ = |
2*4, 000*+50 |
, which rounds to 163 fans per order |
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+15 |
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The number 163 seems strange, so let’s check to see if it results in lower annual costs:
Holding costs + ordering costs
· (163 2)+15 + (4,000 163)+50
· +1,222.50 + +1,226.99 = +2,449.49
Notice that holding costs and ordering costs are essentially equal, as we would expect. More important, simply by ordering the right quantity, you could reduce annual holding and ordering costs for this item by
+7,700 - +2,449 = +5,251
Now suppose Boyer’s carries 250 other products with cost and demand structures sim-ilar to that of the Hudson Valley Model Y ceiling fan. In that case, you might be able to save 250*+5,251 = +1,312,750 per year just by ordering the right quantities!
Of course, the EOQ has some limitations. Holding costs (H) and ordering costs (S) cannot always be estimated precisely, so managers may not always be able to calculate the true EOQ. However, as Figure 11.9 suggests, total holding and ordering costs are relatively flat over a wide range around the EOQ. So order quantities can be off a little and still yield total costs that are close to the minimum.
A more valid criticism of the EOQ is that it does not take into account volume discounts, which can be particularly important if suppliers offer steep discounts to encourage customers to order in large quantities. Later in the chapter, we examine how volume discounts affect the order quantity decision.
338 PART IV • Planning and Controlling Operations and Supply Chains
Other factors that limit the application of the EOQ model include ordering costs that are not always fixed and demand rates that vary throughout the year. However, the EOQ is a good starting point for understanding the impact of order quantities on inventory-related costs.
Table 11.3
Sample Variations in
Demand Rate and Lead Time
Reorder Points and Safety Stock
The EOQ tells managers how much to order but not when to order. We saw in Equation (11.3) that when the demand rate (d) and lead time (L) are constant, the reorder point is easily calculated as:
ROP = dL
But d and L are rarely fixed. Consider the data in Table 11.3, which lists 10 different com-binations of demand rates and lead times. The average demand rate, d, and average lead time, L, are 50 units and 3 weeks, respectively. Our first inclination in this case might be to set the reorder point at d L = 150 units. Yet 5 out of 10 times, dL exceeds 150 units (see Table 11.3). A better solution—one that takes into account the variability in demand rate and lead time—is needed.
When either lead time or demand—or both—varies, a better solution is to set the reorder point higher than ROP = dL. Specifically:
ROP = |
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+ SS |
(11.6) |
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where:
SS = safety stock
Recall that WolfByte Computers carried a safety stock of 1,000 keyboards (Figure 11.4). Again, safety stock (SS) is an extra amount beyond that needed to meet average demand during lead time. This is added to the reorder point to protect against variability in both demand and lead time. Safety stock raises the reorder point, forcing a company to reorder earlier than usual. In doing so, it helps to ensure that future orders will arrive before the existing inventory runs out.
Figure 11.10 shows how safety stock works when both the demand rate and the lead time vary. We start with an inventory level of Q plus the safety stock (Q + SS). When we reach the new reorder point of d L + SS, an order is released. But look what happens during the first reorder pe-riod: Demand exceeds d L, forcing workers to dip into the safety stock. If the safety stock had not been there, the inventory would have run out. In the second reorder period, even though the lead time is longer than before, demand flattens out so much that workers do not need the safety stock.
In general, the decision of how much safety stock to hold depends on five factors:
1. The variability of demand;
2. The variability of lead time;
3. The average length of lead time;
4. The desired service level; and
5. The average demand.
Demand Rate (D) in |
Lead Time (L), |
Demand During |
Units Per Week |
In Weeks |
Lead Time (DL), in Units |
60 |
3 |
180* |
40 |
4 |
160* |
55 |
2 |
110 |
45 |
3 |
135 |
50 |
3 |
150 |
65 |
3 |
195* |
35 |
3 |
105 |
55 |
3 |
165* |
45 |
4 |
180* |
50 |
2 |
100 |
Average = 50 units |
Average = 3 weeks |
Average = 148 units |
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*Demand greater than d L
Figure 11.10
The Impact of Varying
Demand Rates and Lead
Times
CHAPTER 11 • Managing Inventory throughout the Supply Chain 339
Q + SS
1st 2nd
reorder reorder
period period
ROP = dL + SS
SS
Time
Let’s talk about each of these factors. First, the more the demand level and the lead time vary, the more likely it is that inventory will run out. Therefore, higher variability in demand and lead time will tend to force a company to hold more safety stock. Furthermore, a longer av-erage lead time exposes a firm to this variability for a longer period. When lead times are ex-tremely short, as they are in just-in-time (JIT) environments (see Chapter 13), safety stocks can be very small.
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The service level is a managerial decision. Service levels are usually expressed in statisti- |
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cal terms, such as “During the reorder period, we should have stock available 90% of the time.” |
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While the idea that management might agree to accept even a small percentage of stockouts may |
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seem strange, in reality, whenever demand or lead time varies, the possibility exists that a firm |
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will run out of an item, no matter how large the safety stock. The higher the desired service level, |
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the less willing management is to tolerate a stockout, and the more safety stock is needed. |
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EXAMPLE 11.3 |
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Let’s look at one approach to calculating the reorder point with safety stock. Like other |
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Calculating the Reorder |
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approaches, this one is based on simple statistics. To demonstrate the math, we’ll return |
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Point and Safety Stock |
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to Boyer’s Department Store and the Hudson Valley Model Y ceiling fan. Boyer’s sells, on |
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at Boyer’s Department |
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average, 16 Hudson Valley Model Y ceiling fans a day (d = 16), with a standard deviation |
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Store |
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in daily demand of 3 fans (sd |
= 3). This demand information can be estimated easily from |
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past sales history. |
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If the store reorders fans directly from the manufacturer, the fans will take, on average, |
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9 days to arrive (L = 9), with a standard deviation in lead time of 2 days (sL |
= 2). The |
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store manager has decided to maintain a 95% service level. In other words, the manager is |
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willing to run out of fans only 5% of the time before the next order arrives. |
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From these numbers, we can see that: |
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Average demand during the reoder period = |
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= 33.24
To ensure that Boyer’s meets its desired service level, we need to set the reorder point high enough to meet demand during the reorder period 95% of the time. Put another way, the reorder point (ROP) should be set at the ninety-fifth percentile of demand during the reorder period. Because demand during the reorder period is often normally distributed, basic statistics tells us that:
Reorder point (ROP) = ninety-fifth percentile of demand during the reorder period
· d L + zsdL
· 144 + 1.65*33.24
· 198.8, or 199
340 PART IV • Planning and Controlling Operations and Supply Chains
In this equation, 1.65 represents the number of standard deviations (z) above the mean that corresponds to the ninety-fifth percentile of a normally distributed variable. (Other z values and their respective service levels are shown in Table 11.4.) The more gen-eral formula for calculating the reorder point is, therefore:
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+ z |
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sd2 + |
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2sL2 |
(11.8) |
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where: |
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d = average demand per time period |
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L = average lead time |
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= variance of demand per time period |
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= variance of lead time |
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z = number of standard deviations above the average demand during lead time (higher z values lower the probability of a stockout)
Table 11.4 z Values Used in Calculating
Safety Stock
z Value |
Associated Service Level |
0.84 |
80% |
1.28 |
90% |
1.65 |
95% |
2.33 |
99% |
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Notice that the first part of the equation, d L, covers only the average demand during the reorder period. The second part of the equation, z Ls2d + d2s2L, represents the safety stock. For Boyer’s, then, the amount of safety stock needed is:
z Ls2d + d2s2L = 1.65*33.24 = 54.88, or 55 fans
Of course, there are other methods for determining safety stock. Some managers consider variations in both the lead time and the demand rate; others use a definition of service level that includes the frequency of reordering. (Firms that reorder less often than others are less susceptible to stockouts.) In practice, many firms take an unscientific approach to safety stock, such as setting the reorder point equal to 150% of expected demand. Whatever the method used, however, these observations will still hold: The amount of safety stock needed will be affected by the variability of demand and lead time, the length of the average lead time, and the desired service level.
In describing the economic order quantity, one of our assumptions was that the price per unit, P, was fixed. This was a convenient assumption because it allowed us to focus on minimizing just the total holding and ordering costs for the year (Equation [11.3]). But what if a supplier offers a price discount for ordering larger quantities? How will this affect the EOQ?
When quantity discounts are in effect, we must modify our analysis to look at total order-ing, holding, and item costs for the year:
Total holding, ordering, and item costs for the year =
a |
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a Q bS + DP |
(11.9) |
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Q = order quantity |
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D = annual demand |
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P = price per unit (which can now vary)
S = ordering cost
eXaMPle 11.4
volume discounts at hal’s Magic Shop
Chapter 11 • Managing inventory throughout the Supply Chain 341
Because the EOQ formula (Equation [11.5]) considers only holding and ordering costs, the EOQ may not result in lowest total costs when quantity discounts are in effect. To illustrate, sup-pose we have the following information:
D = 1,200 units per year H = +10 per unit per year S = +30 per order
P = +35 per unit for orders less than 90; $32.50 for orders of 90 or more
If we ignore the price discounts and calculate the EOQ, we get the following:
EOQ = |
2*1,200*+30 |
, which rounds to 85 units |
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+10 |
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Total annual holding, ordering, and item costs for an order quantity of 85 are: a 852 b+10 + a 1,20085 b +30 + +35x1200
· +425 + +423.53 + +42,000
· +42,848.53
But note that if we increase the order size by just 5 units, to 90, we can get a discount of
+35 - +32.50 = +2.50 per unit. Selecting an order quantity of 90 would give us the following annual holding, ordering, and item costs:
a 902 b +10 + a 1,20090 b +30 + +32.50x1200
· +450 + +400 + +39,000
· +39,850.00
When volume price discounts are in effect, we must follow a two-step process:
1. Calculate the EOQ. If the EOQ number represents a quantity that can be purchased for the lowest price, stop—we have found the lowest cost order quantity. Otherwise, we go to step 2.
2. Compare total holding, ordering, and item costs at the EOQ quantity with total costs at each price break above the EOQ. There is no reason to look at quantities below the EOQ, as these would result in higher holding and ordering costs, as well as higher item costs.
Robert Landau/Alamy |
342 PART IV • Planning and Controlling Operations and Supply Chains
Hal’s Magic Shop purchases masks from a Taiwanese manufacturer. The manufacturer has quoted the following price breaks to Hal:
Order Quantity |
Price Per Mask |
1–100 |
$15 |
101–200 |
$12.50 |
201 or more |
$10 |
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Hal sells 1,000 masks a year. The cost to place an order is $20, and the holding cost per mask is about $3 per year. How many masks should Hal order at a time?
Solving for the EOQ, Hal gets the following:
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EOQ = |
2*1,000*+20 |
= 115 masks |
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Unfortunately, Hal cannot order 115 masks and get the lowest price of $10 per mask. Therefore, he compares total holding, ordering, and item costs at Q = 115 masks to those at the next price break, which occurs at 201 masks:
Total annual holding, ordering, and item costs for an order quantity of 115 masks =
a 1152 b +3 + a 1,000115 b +20 + +12.50x1000
· +172.50 + +173.91 + +12,500
· +12,846.41
Total annual holding, ordering, and item costs for an order quantity of 201 masks = a 2012 b +3 + a 1,000201 b +20 + +10.00x1000
· +301.50 + +99.50 + +10,000
· +10,401.00
So even though an order quantity of 115 would minimize holding and ordering costs, the price discount associated with ordering 201 masks more than offsets this. Hal should use an order quantity of 201 masks.
11.4 Single-Period Inventory Systems
So far, our discussions have assumed that any excess inventory we order can be held for future use. But this is not always true. In some situations, excess inventory has a very limited life and must be discarded, sold at a loss, or even hauled away at additional cost if not sold in the period intended. Examples include fresh fish, magazines and newspapers, and Christmas trees. In other cases, inventory might have such a specialized purpose (such as spare parts for a specialized ma-chine) that any unused units cannot be used elsewhere. When such conditions apply, a company must weigh the cost of being short against the cost of having excess units, where:
Shortage cost = CShortage = value of the item if demanded - item cost |
(11.10) |
Excess cost = CExcess = item cost + disposal cost - salvage value |
(11.11) |
For example, say that an item that costs $50 sells for $200, but must be disposed of at a cost of $5 if not sold. This item has the following shortage and excess costs:
CShortage = +200 - +50 = +150
CExcess = +50 + +5 = +55
Single-period inventory system
A system used when demand occurs in only a single point in time.
Target service level
For a single-period inventory system, the service level at which the expected cost of a shortage equals the expected cost of having excess units.
Target stocking point
For a single-period inventory system, the stocking point at which the expected cost of a shortage equals the expected cost of having excess units.
CHAPTER 11 • Managing Inventory throughout the Supply Chain 343
The goal of a single-period inventory system is to establish a stocking level that strikes the best balance between expected shortage costs and expected excess costs. Developing a single-period system for an item is a two-step process:
1. Determine a target service level (SLT) that strikes the best balance between shortage costs and excess costs.
2. Use the target service level to determine the target stocking point (TS) for the item.
We describe each of these steps in more detail in the following sections.
For the single-period inventory system, service level is simply the probability that there are enough units to meet demand. Unlike a periodic and continuous review system, there is no re-order period to consider here—either there is enough inventory or there isn’t. The target service level, then, is the service level at which the expected cost of a shortage equals the expected cost of having excess units:
Expected shortage cost = expected excess cost
or:
(1 - p)CShortage = pCExcess |
(11.12) |
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where: |
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p = probability that there are enough units to meet demand |
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(1 - p) = probability that there is a shortage |
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CShortage = shortage cost |
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CExcess = excess cost |
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The target service level (SLT) is the p value at which Equation (11.12) holds true: |
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(1 - SLT)CShortage = SLTCExcess |
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SLT = |
CShortage |
(11.13) |
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CShortage + CExcess |
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where:
CShortage = shortage cost CExcess = excess cost
Let’s use Equation (11.13) to test our intuition. Suppose the shortage cost and the excess cost for an item are both $10. In this case, we would be indifferent to either outcome, and we would set the inventory level so that each outcome would be equally likely. Equation (11.13) confirms our logic:
SLT = |
CShortage |
+10 |
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But what if the cost associated with a shortage is much higher—say, $90? In this case, we |
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would want a much higher target service level because shortage costs are so much more severe |
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than excess costs. Again, Equation (11.13) supports our reasoning: |
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CShortage |
+90 |
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CShortage + CExcess |
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+90 + +10 |
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EXAMPLE 11.5 |
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Don Washing is trying to determine how many gallons of lemonade to make each day. Don |
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Determining the Target |
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needs to consider a single-period system because whatever lemonade is left over at the end |
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Service Level at Don’s |
of the day must be thrown away due to health concerns. Every gallon he mixes costs him |
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Lemonade Stands |
$2.50 but will generate $10 in revenue if sold. |
344 PART IV • Planning and Controlling Operations and Supply Chains
In terms of the single-period inventory problem, Dan’s shortage and excess costs are defined as follows:
CShortage = revenue per gallon - cost per gallon = +10.00 - +2.50 = +7.50
CExcess = cost per gallon = +2.50
From this information, Don can calculate his target service level:
SLT = |
CShortage |
= |
+7.50 |
= 0.75, or 75% |
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CShortage + CExcess |
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+7.50 + +2.50 |
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Interpreting the results, Don should make enough lemonade to meet all demand ap-proximately 75% of the time.
EXAMPLE 11.6 |
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Every day, Fran Chapman of Fran’s Flowers makes floral arrangements for sale at the local |
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Determining the Target |
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hospital. The arrangements cost her approximately $12 to make, but they sell for $25. Any |
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Service Level at Fran’s |
leftover arrangements can be sold at a heavily discounted price of $5 the following day. |
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Flowers |
Fran wants to know what her target service level should be. |
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Fran’s shortage and excess costs are as follows: |
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CShortage |
= revenue per arrangement - cost per arrangement = +25 - +12 = +13 |
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CExcess |
= cost per arrangement - salvage value = +12 - +5 = +7 |
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Fran’s target service level is, therefore: |
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SLT = |
CShortage |
+13 |
= 0.65, or 65% |
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= |
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CShortage + CExcess |
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+13 + +7 |
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Fran should make enough arrangements to meet all demand approximately 65% of |
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the time. |
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To complete the development of a single-period inventory system, we next have to translate the target service level (a probability) into a target stocking point. To do so, we have to know some-thing about how demand is distributed. Depending on the situation, we can approximate the demand distribution from historical records, or we can use a theoretical distribution, such as the normal distribution or Poisson distribution. Furthermore, the distribution may be continuous
(i.e., demand can take on fractional values) or discrete (i.e., demand can take on only integer values). Example 11.7 shows how the process works when we can model demand by using the normal distribution, while Example 11.8 demonstrates the process for a historically based dis-crete distribution.
EXAMPLE 11.7 |
In Example 11.5, Don Washing determined that the target service level for lemonade was: |
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Determining the Target |
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CShortage |
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+7.50 |
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Stocking Point for |
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= |
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= 0.75, or 75% |
Normally Distributed |
CShortage + CExcess |
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+7.50 + +2.50 |
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Demand |
Don knows from past experience that the daily demand follows a normal distribution. |
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Therefore, Don wants to set a target stocking point (TS) that is higher than approximately |
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75% of the area under the normal curve. Figure 11.11 illustrates the idea. |
CHAPTER 11 • Managing Inventory throughout the Supply Chain 345
75%
TS
Figure 11.11 Target Stocking Point for Don’s Lemonade Stands
The general formula for calculating the target stocking point when demand is nor-mally distributed is:
Target stocking point (normally distributed demand) = m + zSLT*s (11.14)
where:
m = mean demand per time period
SLT = number of standard deviations above the mean required to meet the target service level
s = standard deviation of demand per period
To further complicate things, Don also knows that the mean values and standard de-viations for demand differ by day of the week (Table 11.5) . Therefore, he will have to cal-culate different target stocking points for Monday through Friday, Saturday, and Sunday.
Table 11.5 Demand Values for Don’s Lemonade Stands
Day of the Week |
Mean Demand, M |
Standard Deviation of Demand, S |
Monday–Friday |
422 gallons |
67 gallons |
Saturday |
719 gallons |
113 gallons |
Sunday |
528 gallons |
85 gallons |
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Using Equation (11.14) and the cumulative normal table (Table I.2 in Appendix I), Don quickly determines that a service level of 75% would require the target stocking point to be approximately 0.68 standard deviations above the mean. Therefore, the target stock-ing points are as follows:
m + zSLT*s
Monday–Friday: 422 + 0.68*67 = 467.56 gallons
Saturday: 719 + 0.68*113 = 795.84 gallons
Sunday: 528 + 0.68*85 = 585.8 gallons
EXAMPLE 11.8
Determining the Target
Stocking Point for Non-
Normally Distributed
Demand
In Example 11.6, Fran Chapman calculated her target service level for floral arrangements:
CShortage |
= |
+13 |
= 0.65, or 65% |
CShortage + CExcess |
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+13 + +7 |
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Fran has kept track of arrangement sales for the past 34 days and has recorded the de-mand numbers shown in Table 11.6.
346 PART IV • Planning and Controlling Operations and Supply Chains
Table 11.6 Demand History for Fran’s Flowers
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No. of Days With |
Percentage of Days |
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This Demand Level |
Experiencing This |
Cumulative |
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Daily Demand |
During the Past 34 Days |
Demand Level |
Percentage |
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10 or fewer |
0 |
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0 34 = 0% |
0% |
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11 |
2 |
2 |
34 |
= 5.9% |
5.9% |
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12 |
5 |
5 |
34 |
= 14.7% |
20.6% |
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13 |
5 |
5 |
34 |
= 14.7% |
35.3% |
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14 |
6 |
6 |
34 |
= 17.6% |
52.9% |
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15 |
7 |
7 |
34 |
= 20.6% |
73.5% |
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16 |
5 |
5 |
34 |
= 14.7% |
88.2% |
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17 |
3 |
3 |
34 |
= 8.8% |
97.0% |
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18 |
1 |
1 |
34 |
= 2.9% |
100% |
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19 or more |
0 |
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0% |
100% |
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Looking at Table 11.6, Fran realizes that if she wants to meet her target service level of 65%, she will need to stock 15 arrangements each day. This is because 15 arrangements is the first stocking point at which the probability of meeting expected demand (73.5%) is greater than the target service level of 65%. Conversely, if Fran stocked just 14 arrange-ments, according to Table 11.6, she would meet demand only 52.9% of the time.
11.5 Inventory in the Supply Chain
So far, we have discussed the functions and drivers of inventory, and we have identified some basic techniques for managing independent demand inventory items. In this section, we broaden our scope to consider the ramifications of inventory decisions for the rest of the supply chain.
Bullwhip effect
According to APICS, “an extreme change in the supply position upstream in a supply chain generated by a small change in demand down-stream in the supply chain.”
A major limitation of the EOQ model is that it considers the impact on costs for only a single firm. No consideration is given to how order quantity decisions for one firm affect other mem-bers of the supply chain. Therefore, even though the EOQ minimizes costs for a particular firm, it can cause problems for other partners and may actually increase overall supply chain costs. An example of this is the bullwhip effect.5 APICS defines the bullwhip effect as “an extreme change in the supply position upstream in a supply chain generated by a small change in demand down-stream in the supply chain.”6
To illustrate, suppose the ABC plant makes pool cleaners that are sold through six dis-tributors. The distributors have similar demand patterns and identical EOQ and ROP quantities:
Average weekly demand for each distributor = 500 pool cleaners (standard deviation = 100) Reorder quantity for each distributor = 1,500
Reorder point for each distributor = 750
Figure 11.12 shows the results of a simulation covering 50 weeks of simulated demand across the six distributors. Even though total weekly demand across the six distributors ranged from 2,331 to 3,641, the quantities ordered by the distributors to be shipped from the plant ranged from 0 to 7,500 in any one week.
5Hau L. Lee, V. Padmanabhan, and S. Whang, “The Bullwhip Effect in Supply Chain,” Sloan Management Review 38, no. 3 (Spring 1997): 70–77.
6Definition of Bullwhip Effect in J. H. Blackstone, ed., APICS Dictionary, 14th ed. (Chicago, IL: APICS, 2013). Reprinted by permission.
Figure 11.12
Total Demand across the Six Distributors
Resulting Total Quantities
(Q = 1,500 for Each
Distributor) Ordered from
the ABC Plant
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CHAPTER 11 • Managing Inventory throughout the Supply Chain |
347 |
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4,000 |
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3,500 |
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3,000 |
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Demand |
2,500 |
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2,000 |
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1,500 |
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1,000 |
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500 |
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0 |
1 |
3 |
5 |
7 |
9 |
11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 |
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Week |
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quantity |
8,000 |
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6,000 |
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order |
4,000 |
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Total |
2,000 |
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0 |
1 |
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4 |
7 |
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10 |
13 |
16 |
19 |
22 |
25 |
28 |
31 |
34 |
37 |
40 |
43 |
46 |
49 |
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Week |
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Figure 11.13
Resulting Total Quantities
(Q = 750 for Each
Distributor) Ordered from
the ABC Plant
What causes this? Quite simply, if a distributor reaches its reorder point, it places a large order. Otherwise, it does nothing. Therefore, a single-unit change in demand may de-termine whether a distributor places an order. So even though the distributors may be fol-lowing good inventory practice by ordering in quantities of 1,500, the impact on the supply chain is to increase demand variability at the plant. Ultimately, this demand variability will drive up costs at the plant, which will then be forced to pass on at least some of these costs to the distributors.
In order to reduce the bullwhip effect, many supply chain partners are working together to reduce order quantities by removing volume discount incentives and reducing ordering costs. Figure 11.13 shows, for example, what the quantities ordered from the plant would look like if order quantities were cut in half, to 750. Now the orders range from 750 to 4,500; this is not per-fect, but it’s a big improvement over what the range was before.
Managers must decide where in the supply chain to hold inventory. In general, the decision about where to position inventory is based on two general truths:
1. The cost and value of inventory increase as materials move down the supply chain.
2. The flexibility of inventory decreases as materials move down the supply chain.
That is, as materials work their way through the supply chain, they are transformed, pack-aged, and moved closer to their final destination. All these activities add both cost and value.
quantity |
5,000 |
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4,000 |
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3,000 |
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order |
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2,000 |
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Total |
1,000 |
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0 |
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1 |
4 |
7 |
10 |
13 |
16 |
19 |
22 |
25 |
28 |
31 |
34 |
37 |
40 |
43 |
46 |
49 |
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Week |
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348 PART IV • Planning and Controlling Operations and Supply Chains
Take breakfast cereal, for example. By the time it reaches the stores, cereal has gone through such a significant transformation and repackaging that it appears to have little in common with the basic materials that went into it. But the value added goes beyond transformation and pack-aging; it includes location as well. A product that is in stock and available immediately is always worth more to the customer than the same product available later.
What keeps organizations from pushing inventory as far down the supply chain as pos-sible? Cost, for one thing. By delaying the transformation and movement of materials, orga-nizations can postpone the related costs. Another reason for holding inventory back in the supply chain is flexibility. Once materials have been transformed, packaged, and transported down the chain, reversing the process becomes very difficult, if not impossible. Wheat that has been used to make a breakfast cereal cannot be changed into flour that is suitable for making a cake. Likewise, repackaging shampoo into a different-sized container is impractical once it has been bottled. The same goes for transportation: Repositioning goods from one location to an-other can be quite expensive, especially compared to the cost of delaying their movement until demand has become more certain. This loss of flexibility is a major reason materials are often held back in the supply chain. In short, supply chain managers are constantly trying to strike a balance between costs on the one hand and flexibility on the other in deciding where to posi-tion inventory.
EXAMPLE 11.9 |
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An especially good case for holding back inventory can be made if an organization can |
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Pooling Safety Stock at |
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hold all of its safety stock in a single central location while still providing reasonably fast |
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Boyer’s Department Store |
service to customers. This is one example of inventory pooling, in which several locations |
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Inventory pooling |
share safety stock inventories in order to lower overall holding costs. Suppose, for instance, |
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that Boyer’s has eight stores in the Chicago area. Each store sells, on average, 10 ceiling |
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Holding safety stock in a single |
fans a day. Suppose that the standard deviation of daily demand at each store is 3 (sd = 3) |
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location instead of multiple lo- |
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and the average lead time from the fan manufacturer is 9 days, with a standard deviation |
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cations. Several locations then |
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share safety stock inventories |
of 2 days. We showed in Example 11.3 that to maintain a 95% service level (z = 1.65), a |
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to lower overall holding costs |
store would need to maintain a safety stock of 55 fans. The total safety stock across all eight |
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by reducing overall safety stock |
stores would therefore be 8*55 = 440 fans. |
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levels. |
But what if Boyer’s could pool the safety stock for all eight stores at a single store, |
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which could provide same-day service to the other seven stores? Because a single location |
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would have a demand variance equal to n times that of n individual stores: |
Standard deviation of demand during lead time, across n locations = n*sdL
For Boyer’s, this calculates out to:
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= |
8* |
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2*sL2 |
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L |
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d |
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= |
8*33.24 |
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= 94 fans
And the pooled safety stock would be:
z*94 = 1.65*94 = 155.1, or 155 fans
By pooling its safety stock, Boyer’s could reduce the safety stock level by (440 - 155) = 285 fans, or 65%. Considering the thousands of items stocked in Boyer’s eight stores, centralizing Boyer’s safety stock could produce significant savings.
Transportation, Packaging, and Material Handling Considerations
We will wrap up our discussion of inventory in the supply chain by considering how inventory decisions—most notably, order quantities—are intertwined with transportation, packaging, and material handling issues. The point of this discussion is to recognize that, in the real world, there is more to determining order quantities than just holding, ordering, and item costs.
Chapter 11 • Managing inventory throughout the Supply Chain 349
SupplY Chain ConneCtIons
inventOrY ManageMent anD pOOling grOupS at autOMOtive DealerShipS
Evans/AlamyBalfour |
Greg |
Automobile dealerships face a classic dilemma in deciding how to manage their inventories of service parts. On the one hand, customers expect their cars to be fixed promptly. On the other hand, dealerships
typically do not have the space or financial resources to stock all the possible items a customer’s car may need. If this wasn’t difficult enough, most dealerships do not have the inventory expertise on site to deal with these issues.
To address these concerns, many automotive manufacturers have developed information systems in which the manufacturer makes inventory decisions for dealerships, based on calculated reorder points. Of course, the dealerships may override these recommen-dations if they like. And if a part placed in the dealer-ship under the recommendation of the system sits at the dealership too long, the manufacturer will typically buy it back.
In addition, dealerships in the same geographic region typically establish “pooling groups.” These deal-erships agree to share safety stocks for expensive or slow- moving items. If one dealership runs out of the part, it can instantly check on the part’s availability within the pooling group (via an information system) and arrange to have the item picked up. The result is lower overall inventories and better parts availability for customers.
Consider an example. Borfax Industries buys specialized chemicals from a key supplier. These chemicals can be purchased in one of two forms:
|
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DiMenSiOnalitY |
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fOrM |
QuantitY |
Weight |
(WiDth/Depth/height) |
priCe per Bag |
Carton |
144 bags |
218 lb. |
2 * 2 * 1 |
$25 |
pallet |
12 cartons (1,728 bags) |
2,626 lb. |
4 * 4 * 3.5 |
$18 |
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First, notice that the chemicals can be purchased in multiples of 144 bags per carton or 1,728 bags per pallet. It is highly unlikely that any EOQ value calculated by Borfax will fit per-fectly into either of these packaging alternatives.
If Borfax purchases a full pallet, it can get a substantial price discount. The supplier will also make a direct truck shipment if Borfax purchases five or more pallets at a time. This will reduce the lead time from 15 days to 5. However, pallets require material handling equipment capable of carrying nearly 3,000 pounds, as well as suitable storage space. On the other hand, the cartons are less bulky but will still require some specialized handling due to their weight. In choosing the best order quantity, Borfax must not only look at the per-bag price but also con-sider its material handling capabilities, transportation costs, and inventory holding costs.
Inventory is an important resource in supply chains, serving many functions and taking many forms. But like any other resource, it must be managed well if an organization is to remain competi-tive. We started this chapter by examining the various types of inventory in a simple supply chain. We also discussed what drives inventory. To the extent that organizations can leverage inventory drivers, they can bring down the amount of inventory they need to hold in order to run their supply chains smoothly.
In the second part of this chapter, we introduced some basic tools for managing independent demand inventory. These tools provide managers with simple models for determin-ing how much to order and when to order. We then examined the relationship between inventory decisions and the bullwhip effect, the decision about where to position inventory in the supply chain, and how transportation, packaging, and material handling considerations might impact inventory decisions.
350 PART IV • Planning and Controlling Operations and Supply Chains
Restocking level under a periodic review system (page 334):
|
R = mRP + L + zsRP + L |
(11.2) |
where: |
= average demand during the reorder period and the order lead time |
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mRP + L |
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sRP + L |
= standard deviation of demand during the reorder period and the order lead time |
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z = number of standard deviations above the average demand (higher z values increase the restocking level, thereby lowering the probability of a stockout)
Total holding and ordering costs for the year (page 336):
aQ2 bH + a DQ bS
where:
Q = order quantity
H = annual holding cost per unit D = annual demand
S = ordering cost
Economic order quantity (EOQ) (page 336):
(11.4)
Q =
where:
Q = order quantity
H = annual holding cost per unit D = annual demand
S = ordering cost
2 DS = EOQ
H
(11.5)
Reorder point under a continuous review system (page 340): |
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ROP = |
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+ z |
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2sL2 |
(11.8) |
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d |
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where: |
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d = average demand per time period |
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sd2 |
= variance of demand per time period |
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sL2 |
= variance of lead time |
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z = number of standard deviations above the average demand during lead time (higher z values lower the probability of a stockout)
Total holding, ordering, and item costs for the year (page 340):
a Q2 bH + a DQ bS + DP
where:
Q = order quantity
H = holding cost per unit D = annual demand
P = price per unit S = ordering cost
Target service level under a single-period inventory system (page 343):
CShortage
SLT = CShortage + CExcess
where:
CShortage = shortage cost CExcess = excess cost
(11.9)
(11.13)
CHAPTER 11 • Managing Inventory throughout the Supply Chain 351
Anticipation inventory 330 |
Hedge inventory |
Service level 334 |
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Bullwhip effect 346 |
Independent demand inventory 333 |
Single-period inventory system 343 |
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Continuous review system 335 |
Inventory 328 |
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Smoothing inventory | ||
Cycle stock 329 |
Inventory drivers |
Supply uncertainty | |||
Demand uncertainty 332 |
Inventory pooling |
Target service level | |||
Dependent demand inventory 333 |
Periodic review system 333 |
Target stocking point | |||
Economic order quantity (EOQ) 336 |
Safety stock 329 |
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Transportation inventory 330 |
Using Excel in Inventory Management
Several of the models described in this chapter depend on estimates of average demand and average lead time and on associated measures of variance (s2) or standard deviation (s). The spreadsheet model in Figure 11.14 shows how such values can be quickly estimated from historical data, using Microsoft Excel’s built-in functions. The spreadsheet contains historical
demand data for 20 weeks, as well as lead time information for 15 prior orders. From this information, the spreadsheet calcu-lates average values and variances and then uses these values to calculate average demand during lead time, safety stock, and the reorder point. The highlighted cells represent the input values. The calculated cells are as follows:
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Cell C32 (average weekly demand): |
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= AVERAGE(C12:C31) |
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Cell C33 (variance of weekly demand): |
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Cell G27 (average order lead time): |
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Cell G28 (variance of lead time): |
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Cell F5 (average demand during lead time): |
= C32*G27 |
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Cell F6 (safety stock): |
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= F3*SQrt(g27*C33 +C32^2*g28) |
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Cell F7 (reorder point): |
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= F5+F6 |
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E |
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Calculating the Reorder Point from Demand and Order History |
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z value (for desired service level:) |
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1.65 |
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4 |
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5 |
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280.72 |
units |
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Average demand during lead time: |
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6 |
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+ Safety stock: |
125.47 |
units |
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7 |
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Reorder point: |
406.19 |
units |
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(Equation 10 |
-6) |
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8 |
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9 |
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*** |
Demand History |
*** |
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*** Order History |
*** |
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10 |
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Lead time |
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Week |
Demand |
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Order |
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(days) |
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12 |
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1 |
33 |
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1 |
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10 |
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13 |
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2 |
14 |
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2 |
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6 |
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14 |
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3 |
18 |
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3 |
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12 |
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15 |
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4 |
37 |
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4 |
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9 |
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16 |
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5 |
34 |
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5 |
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10 |
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17 |
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6 |
53 |
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6 |
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8 |
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18 |
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7 |
31 |
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7 |
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8 |
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19 |
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8 |
21 |
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8 |
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8 |
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20 |
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9 |
19 |
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9 |
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7 |
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21 |
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10 |
44 |
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10 |
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3 |
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22 |
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11 |
43 |
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11 |
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8 |
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23 |
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12 |
37 |
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12 |
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9 |
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24 |
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13 |
45 |
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13 |
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7 |
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25 |
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14 |
43 |
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14 |
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8 |
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26 |
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15 |
36 |
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15 |
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8 |
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27 |
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16 |
40 |
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Average: |
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8.07 |
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28 |
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17 |
28 |
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Variance: |
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4.07 |
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29 |
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18 |
41 |
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30 |
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19 |
36 |
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31 |
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20 |
43 |
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32 |
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Average: |
34.80 |
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33 |
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Variance: |
106.27 |
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Figure 11.14 Excel Solution to the Reorder Point Problem
352 PART IV • Planning and Controlling Operations and Supply Chains
P r o b l e m 1
P r o b l e m 2
Jake Fleming sells graphic card update kits for computers. Jake purchases these kits for $20 and sells about 250 kits a year. Each time Jake places an order, it costs him $25 to cover shipping and paperwork. Jake figures that the cost of holding an update kit in inventory is about $3.50 per kit per year. What is the economic order quantity? How many times per year will Jake place an order? How much will it cost Jake to order and hold these kits each year?
Solution
The economic order quantity for the kits is:
2*250*+25 = 59.76, or 60 kits +3.50
The number of orders placed per year is:
25060 = 4.17 orders per year
The total holding and ordering costs for the year (not counting any safety stock Jake might hold) are:
602+3.50 + 25060+25 = +105 + +104.17 = +209.17
The manufacturer of the graphic card update kits has agreed to charge Jake just $15 per kit if Jake orders 250 kits at a time. Should Jake accept the manufacturer’s offer?
Solution
For the EOQ, the total holding, ordering, and item costs for the year are:
60 |
+3.50 + |
250 |
+25 = |
250*+20 = |
+105 + +104.17 + +5,000 = +5,209.17 |
2 |
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60 |
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|
If Jake takes the volume discount, he will order 250 kits at a time (after all, ordering more than 250 would only move him farther away from the EOQ, which minimizes holding and or-dering costs):
2502+3.50 + 250250+25 + 250*+15 = +437.50 + +25 + +3,750 = +4,212.50 Therefore, Jake should take the volume discount and order just once a year.
1. You hear someone comment that any inventory is a sign of waste. Do you agree or disagree? Can managers simulta-neously justify holding inventories and still seek out ways to lower inventory levels?
2. In your own words, what is an inventory driver? What is the difference between a controllable inventory driver and an uncontrollable inventory driver? Give examples.
3. Which of the following are independent demand inven-tory items? Dependent demand inventory items?
a. Bicycles in a toy store
b. Bicycle wheels in a bicycle factory
c. Blood at a blood bank
d. Hamburgers at a fast-food restaurant
e. Hamburger buns at a plant that produces frozen dinners
4. In a supply chain, what are the pros and cons of pushing inventory downstream, closer to the final customer? How might modular product designs (Chapter 15) make it
more profitable for companies to postpone the movement of inventory down the supply chain?
5. (Use the EOQ and ROP formulas to answer this question.) Which variables could you change if you wanted to reduce inventory costs in your organization? Which ones would you prefer to change? Why?
6. The JIT/lean production movement has long argued that firms should:
a. Maximize their process flexibility so that ordering costs are minimized;
b. Stabilize demand levels;
c. Shrink lead times as much as possible; and
d. Assign much higher holding costs to inventory than has traditionally been the case.
Using the EOQ and ROP formulas, explain how such efforts would be consistent with JIT’s push for lower inventory levels.
CHAPTER 11 • Managing Inventory throughout the Supply Chain 353
(* = easy; ** = moderate; *** = advanced)
Problems for Section 11.2: Periodic Review Systems
1. Jimmy’s Delicatessen sells large tins of Tom Tucker’s Tof-fee. The deli uses a periodic review system, checking in-ventory levels every 10 days, at which time an order is placed for more tins. Order lead time is 3 days. Average daily demand is 7 tins, so average demand during the re-order period and order lead time (13 days) is 91 tins. The standard deviation of demand during this same 13-day period is 17 tins.
a. (*) Calculate the restocking level. Assume that the de-sired service level is 90%.
b. (**) Suppose that the standard deviation of demand during the 13-day period drops to 4 tins. What hap-pens to the restocking level? Explain why.
c. (***) Draw a sawtooth diagram similar to the one in Figure 11.3. Assume that the beginning inven-tory level is equal to the restocking level and that the demand rate is a constant 7 tins per day. What is the safety stock level? (Hint: Look at the formula for cal-culating restocking level.) What is the average inven-tory level?
2. Mountain Mouse makes freeze-dried meals for hikers. One of Mountain Mouse’s biggest customers is a sport-ing goods superstore. Every 5 days, Mountain Mouse checks the inventory level at the superstore and places an order to restock the meals. These meals are delivered by UPS in 2 days. Average demand during the reorder period and order lead time is 100 meals, and the stan-dard deviation of demand during this same time period is about 20 meals.
a. (**) Calculate the restocking level for Mountain Mouse. Assume that the superstore wants a 90% service level. What happens to the restocking level if the superstore wants a higher level of service—say, 95%?
b. (*) Suppose there are 20 meals in the superstore when Mountain Mouse checks inventory levels. How many meals should be ordered, assuming a 90% service level?
Problems for Section 11.3: Continuous Review Systems
3. Pam runs a mail-order business for gym equipment. An-nual demand for TricoFlexers is 16,000. The annual hold-ing cost per unit is $2.50, and the cost to place an order is $50.
a. (*) What is the economic order quantity?
b. (**) Suppose demand for TricoFlexers doubles, to 32,000. Does the EOQ also double? Explain what happens.
c. (**) The manufacturer of TricoFlexers has agreed to of-fer Pam a price discount of $5 per unit ($45 rather than $50) if she buys 1,500. Assuming that annual demand is still 16,000, how many units should Pam order at a time?
4. KraftyCity is a large retailer that sells power tools and other hardware supplies. One of its products is the
KraftyMan workbench. Information on the workbench is as follows:
Annual demand = 1,200
Holding cost = +15 per year
Ordering cost = +200 per order
a. (*) What is the economic order quantity for the workbench?
b. (**) Suppose that KraftyCity has to pay $50 per work-bench for orders under 200 but only $42 per workbench for orders of 201 or more. Using the information pro-vided above, what order quantity should KraftyCity use?
c. (*) The lead time for KraftyCity workbenches is 3 weeks, with a standard deviation of 1.2 weeks, and the average weekly demand is 24, with a standard devi-ation of 8 workbenches. What should the reorder point be if KraftyCity wants to provide a 95% service level?
d. (**) Now suppose the supplier of workbenches guar-antees KraftyCity that the lead time will be a constant 3 weeks, with no variability (i.e., standard deviation of lead time = 0). Recalculate the reorder point, using the demand and service level information in problem c. Is the reorder point higher or lower? Explain why.
5. Ollah’s Organic Pet Shop sells about 4,000 bags of free-range dog biscuits every year. The fixed ordering cost is $15, and the cost of holding a bag in inventory for a year is $2.
a. (*) What is the economic order quantity for the biscuits?
b. (**) Suppose Ollah decides to order 200 bags at a time. What would the total ordering and holding costs for the year be? (For this problem, don’t consider safety stock when calculating holding costs.)
c. (**) Average weekly demand for free-range dog biscuits is 80 bags per week, with a standard deviation of 16 bags. Ollah uses a continuous inventory review system to manage inventory of the biscuits. Ollah wants to set the reorder point high enough that there is only a 5% chance of running out before the next order comes in. Assuming that the lead time is a constant 2 weeks, what should the reorder point be?
d. (**) Suppose Ollah decides to use a periodic review system to manage the free-range dog biscuits, with the vendor checking inventory levels every week. Under this scenario, what would the restocking level be, assuming the same demand and lead time characteristics listed in problem 13 and the same 95% service level? (Note that because the standard deviation of weekly demand is 16,
basic statistics tells us the standard deviation of demand over 3 weeks will be 3 * 16 ≈ 28.)
6. Ollah’s Organic Pet Shop sells bags of cedar chips for pet bedding or snacking (buyer’s choice). The supplier has of-fered Ollah the following terms:
Order 1–100 bags, and the price is $6.00 a bag. Order 101 or more bags, and the price is $4.50 a bag.
Annual demand is 630, fixed ordering costs are $9 per order, and the per-bag holding cost is estimated to be around $2 per year.
354 PART IV • Planning and Controlling Operations and Supply Chains
a. (*) What is the economic order quantity for the bags?
b. (**) What order quantity should Ollah order, based on the volume discount? Is this different from the EOQ? If so, how could this be?
c. (**) Suppose the lead time for bags is a constant 2 weeks, and average weekly demand is 12.6 bags, with a standard deviation of 3.2 bags. If Ollah wants to maintain a 98% service level, what should her reorder point be?
Problems for Section 11.4: Single-Period Inventory Systems
7. (**) David Polston prints up T-shirts to be sold at local con-certs. The T-shirts sell for $20 each but cost David only $6.50 each. However, because the T-shirts have concert-specific in-formation on them, David can sell a leftover shirt for only $3. Suppose the demand for shirts can be approximated with a normal distribution and the mean demand is 120 shirts, with a standard deviation of 35. What is the target service level? How many shirts should David print up for a concert?
8. Sherry Clower is trying to figure out how many custom books to order for her class of 25 students. In the past, the number of students buying books has shown the following demand pattern:
Number of Students |
Percentage of |
Who Bought a Book |
Observations |
16 or fewer |
0% |
17 |
4% |
18 |
15% |
19 |
17% |
20 |
18% |
21 |
26% |
22 |
10% |
23 |
6% |
24 |
4% |
25 |
0% |
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|
a. (**) Suppose each custom book costs Sherry $12 to print, and she sells the books to the students for $50 each. Excess books must be scrapped. What is the tar-get service level? What is the target stocking point?
b. (**) Suppose printing costs increase to $22. Recalculate the new target service level and target stocking point. What happens?
9. One of the products sold by OfficeMax is a Hewlett-Pack-ard LaserJet Z99 printer. As purchasing manager, you have the following information for the printer:
Average weekly demand |
60 printers |
(52 weeks per year) |
|
Standard deviation of weekly |
12 printers |
demand |
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Order lead time |
3 weeks |
Standard deviation of order |
0 (lead times are constant) |
lead time |
|
Item cost |
$120 per printer |
Cost to place an order |
$2 |
Yearly holding cost per printer |
$48 |
Desired service level during |
99% (z = 2.33) |
reordering period |
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a. (*) What is the economic order quantity for the printer?
b. (**) Calculate annual ordering costs and holding costs (ignoring safety stock) for the EOQ. What do you no-tice about the two?
c. (**) Suppose OfficeMax currently orders 120 printers at a time. How much more or less would OfficeMax pay in holding and ordering costs per year if it ordered just 12 printers at a time? Show your work.
d. (**) What is the reorder point for the printer? How much of the reorder point consists of safety stock?
For parts e and f, use the following formula to consider the impact of safety stock (SS) on average inventory levels and annual holding costs:
a Q 2 + SSbH
e. (***) What is the annual cost of holding inventory, in-cluding the safety stock? How much of this cost is due to the safety stock?
f. (***) Suppose OfficeMax is able to cut the lead time to a constant 1 week. What would the new safety stock level be? How much would this reduce annual holding costs?
10. (***) OfficeMax is considering using the Internet to order printers from Hewlett-Packard. The change is expected to make the cost of placing orders drop to almost nothing, although the lead time will remain the same. What effect will this have on the order quantity? On the holding and ordering costs for the year? Explain using any formulas and examples you find helpful.
11. Through its online accessory store, Gateway sells its own products, as well as products made by other companies. One of these products is the WB150 WolfByte laptop computer:
Estimated annual demand |
15,376 laptops |
|
(50 weeks per year) |
Cost |
$640 per laptop |
Lead time |
2 weeks |
Standard deviation of weekly |
16 laptops |
demand |
|
Standard deviation of lead time |
0.3 weeks |
Holding cost per unit per |
40% of item cost |
year |
|
Ordering cost |
$25 per order |
Desired service level |
95% (z = 1.65) |
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|
a. (*) What is the economic order quantity for the lap-tops? Calculate annual ordering costs and holding costs (ignoring safety stock) for the EOQ.
b. (**) What is the reorder point for the laptops? How much of the reorder point consists of safety stock?
c. (**) Suppose Gateway decides to order 64 laptops at a time. What would its yearly ordering and holding costs (ignoring safety stock) for the monitor be?
d. (**) Because computer technologies become obsolete so quickly, Gateway is thinking about raising holding costs from 40% of item cost to some higher percentage. What will be the impact on the economic order quan-tity for laptops? Explain why.
CHAPTER 11 • Managing Inventory throughout the Supply Chain 355
For parts e and f, use the following formula to consider the impact of safety stock (SS) on average inventory levels and annual holding costs:
aQ2 + SSbH
e. (***) What is the annual cost of holding inventory, in-cluding the safety stock? How much of this cost is due to the safety stock?
f. (***) Suppose Gateway is able to cut the lead time to a constant 1 week. What would the new safety stock level be? How much would this reduce annual holding costs?
12. One of the products stocked by a Sam’s Club store is Sams Cola, which is sold in cases. The demand level for Sams Cola is highly seasonal:
· During the slow season, the demand rate is approxi-mately 650 cases a month, which is the same as a yearly demand rate of 650*12 = 7,800 cases.
· During the busy season, the demand rate is approxi-mately 1,300 cases a month, or 15,600 cases a year.
· The cost to place an order is $5, and the yearly holding cost for a case of Sams Cola is $12.
a. (**) According to the EOQ formula, how many cases of Sams Cola should be ordered at a time during the slow season? How many cases of Sams Cola should be ordered during the busy season?
b. (**) Suppose Sam’s Club decides to use the same or-der quantity, Q = 150, throughout the year. Calculate total holding and ordering costs for the year. Do not consider safety stock in your calculations. (Annual de-mand can be calculated as an average of the slow and busy rates given above.)
13. (**) During the busy season, the store manager has de-cided that 98% of the time, she does not want to run out of Sams Cola before the next order arrives. Use the following data to calculate the reorder point for Sams Cola:
Weekly demand during the |
325 cases per week |
busy season |
|
Lead time |
0.5 weeks |
Standard deviation of |
5.25 |
weekly demand |
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Standard deviation of lead |
0 (lead time is constant) |
time |
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Number of standard |
2.05 |
deviations above the mean |
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needed to provide a 98% |
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service level (z) |
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14. (**) Dave’s Sporting Goods sells Mountain Mouse freeze-dried meals. Dave’s uses a continuous review system to manage meal inventories. Suppose Mountain Mouse offers the following volume discounts to its customers:
1–500 meals: $7 per meal
501 or more meals: $6.50 per meal
Annual demand is 2,000 meals, and the cost to place an order is $15. Suppose the holding cost is $2 per meal per year. How many meals should Dave’s order at a time? What are the total holding, ordering, and item costs asso-ciated with this quantity?
15. (***) (Microsoft Excel problem) The following figure shows an Excel spreadsheet that compares total ordering and holding costs for some current order quantity to the same costs for the EOQ and calculates how much could be saved by switching to the EOQ. Re-create this spreadsheet in Excel. You should develop the spreadsheet so that the re-sults will be recalculated if any of the values in the high-lighted cells are changed. Your formatting does not have to be exactly the same, but the numbers should be. (As a test, see what happens if you just change the annual demand and cost per order to 5,000 and $25, respectively. Your new EOQ should be 91.29, and the total savings under the EOQ should be $5,011.39.)
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B |
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1 |
Calculating Savings under EOQ |
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2 |
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3 |
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Annual demand: |
4,000 |
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4 |
Annual holding cost, per unit: |
$30.00 |
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5 |
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Cost per order: |
$30.00 |
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6 |
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7 |
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Current order quantity: |
500 |
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8 |
Current annual holding cost: |
$7,500.00 |
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9 |
Current annual ordering cost: |
$240.00 |
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10 |
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Total cost: |
$7,740.00 |
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11 |
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12 |
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Economic order quantity: |
89.44 |
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13 |
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EOQ annual holding cost: |
$1,341.64 |
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14 |
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EOQ annual ordering cost: |
$1,341.64 |
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15 |
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Total cost: |
$2,683.28 |
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16 |
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17 |
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Total savings under EOQ: |
$5,056.72 |
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18 |
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Problems for Section 11.5: Inventory in the Supply Chain
16. (***) (Microsoft Excel problem) The following figure shows an Excel spreadsheet that calculates the benefit of pool-ing safety stock. Specifically, the sheet calculates how much could be saved in annual holding costs if the safety stocks for three locations were held in a single location.
Re-create this spreadsheet in Excel. You should develop the spreadsheet so that the results will be recalculated if any of the values in the highlighted cells are changed. Your formatting does not have to be exactly the same, but the numbers should be. (As a test, see what happens if you change Location 1’s average daily demand and variance of daily demand to 100 and 15, respectively. Your new pooled safety stock should be 30.34, and the total savings due to pooling safety stock should be $108.21.)
356 part iv • planning and Controlling operationS and Supply ChainS
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B |
C |
D |
E |
F |
G |
1 |
Calculating Savings Due to Pooling Safety Stock |
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2 |
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3 |
Annual holding cost per unit: |
$5.00 |
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4 |
Lead time ( xed): |
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8 |
days |
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5 |
z value (for desired service level): |
2.33 |
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6 |
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7 |
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Average demand |
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8 |
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Average |
Variance of |
Reorder |
during |
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9 |
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|
daily demand |
daily demand |
point |
lead time |
Safety stock |
10 |
|
Location 1 |
50 |
4.5 |
413.98 |
400.00 |
13.98 |
11 |
|
Location 2 |
40 |
6.2 |
336.41 |
320.00 |
16.41 |
12 |
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Location 3 |
30 |
5 |
254.74 |
240.00 |
14.74 |
13 |
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Total units: |
45.13 |
14 |
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Total annual |
holding cost: |
$225.63 |
15 |
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16 |
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Average demand |
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17 |
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Average |
Variance of |
Reorder |
during |
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18 |
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daily demand |
daily demand |
point |
lead time |
Safety stock |
19 |
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Pooled SS |
120 |
15.7 |
986.11 |
960.00 |
26.11 |
20 |
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Total annual holding cost: |
$130.56 |
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21 |
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22 |
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Savings due to pooling safety stock: |
$95.07 |
northcutt Bikes: the Service Department
Ievgen Sosnytskyi/Shutterstock |
Introduction
Several years ago, Jan Northcutt, owner of Northcutt Bikes, recognized the need to organize a separate department to deal with service parts for the bikes her company makes. Because the competitive strength of her company was developed around customer responsiveness and flexibility, she felt that creating a separate department focused exclusively on aftermarket service was critical in meeting that mission.
When she established the department, she named Ann Hill, one of her best clerical workers at the time, to establish and man-age the department. At first, the department occupied only a cor-ner of the production warehouse, but now it has grown to occupy its own 100,000-square-foot warehouse. The service business has also grown significantly, and it now represents over 15% of the total revenue of Northcutt Bikes. The exclusive mission of the service department is to provide parts (tires, seats, chains, etc.) to the many retail businesses that sell and service Northcutt Bikes.
While Ann has turned out to be a very effective manager (and now holds the title of Director of Aftermarket Service), she still lacks a basic understanding of materials management. To help her develop a more effective materials management program, she hired Mike Alexander, a recent graduate of an outstanding business management program at North Carolina State University, to fill the newly created position of Materials Manager of Aftermarket Service.
The Current Situation
During the interview process, Mike got the impression that there was a lot of opportunity for improvement at Northcutt Bikes. It was only after he selected his starting date and re-quested some information that he started to see the full extent of the challenges that lay ahead. His first day on the job really opened his eyes. One of the first items he had requested was a status report on inventory history and shipped orders. In re-sponse, the following note was on his desk the first day from the warehouse supervisor, Art Demming:
We could not compile the history you requested, as we keep no such records. There’s just too much stuff in here to keep a close eye on it all. Rest assured, however, that we think the inventory positions on file are accurate, as we just completed our physi-cal count of inventory last week. I was able to track down a demand history for a couple of our items, and that is attached to this memo. Welcome to the job!
CHAPTER 11 • Managing Inventory throughout the Supply Chain 357
Mike decided to investigate further. Although the records were indeed difficult to track down and compile, by the end of his second week, he had obtained a fairly good picture of the situation, based on an investigation of 100 parts selected at ran-dom. He learned, for example, that although there was an aver-age of over 70 days’ worth of inventory (annual sales/average inventory), the fill rate for customer orders was less than 80%, meaning that only 80% of the items requested were in inven-tory; the remaining orders were backordered. Unfortunately, the majority of customers viewed service parts as generic and would take their business elsewhere when parts were not avail-able from Northcutt Bikes.
What really hurt was when those businesses sometimes canceled their entire order for parts and placed it with another parts supplier. The obvious conclusion was that while there was plenty of inventory overall, the timing and quantities were misplaced. Increasing the inventory did not appear to be the answer, not only because a large amount was already being held but also because the space in the warehouse (built less than two years ago) had increased from being 45% utilized just after they moved in to its present utilization of over 95%.
Mike decided to start his analysis and development of so-lutions on the two items for which Art had already provided demand history. He felt that if he could analyze and correct any problems with those two parts, he could expand the analysis to most of the others. The two items on which he had history and concentrated his initial analysis were the FB378 Fender Bracket and the GS131 Gear Sprocket. Northcutt Bikes purchases the FB378 from a Brazilian source. The lead time has remained constant, at three weeks, and the estimated cost of a purchase order for these parts is given at $35 per order. Currently North-cutt Bikes uses an order lot size of 120 for the FB378 and buys the items for $5 apiece.
The GS131 part, on the other hand, is a newer prod-uct only recently being offered. A machine shop in Nashville, Tennessee, produces the part for Northcutt Bikes, and it gives Northcutt Bikes a fairly reliable six -week lead time. The cost of placing an order with the machine shop is only about $15, and currently Northcutt Bikes orders 850 parts at a time. Northcutt Bikes buys the item for $10.75.
Following is the demand information that Art gave to Mike on his first day for the FB378 and the GS131:
|
FB378 |
|
GS131 |
|
|
|
|
|
Actual |
|
Actual |
Week |
Forecast |
Demand |
Forecast |
Demand |
|
6 |
38 |
|
30 |
|
|
7 |
36 |
|
26 |
|
|
8 |
33 |
|
45 |
|
|
9 |
37 |
|
33 |
|
|
10 |
37 |
|
30 |
|
|
11 |
36 |
|
47 |
10 |
16 |
12 |
37 |
|
40 |
18 |
27 |
13 |
38 |
|
31 |
30 |
35 |
14 |
36 |
|
38 |
42 |
52 |
15 |
36 |
|
32 |
55 |
51 |
16 |
35 |
|
49 |
54 |
44 |
17 |
37 |
|
24 |
52 |
57 |
18 |
35 |
|
41 |
53 |
59 |
19 |
37 |
|
34 |
53 |
46 |
20 |
36 |
|
24 |
52 |
62 |
21 |
34 |
|
52 |
53 |
51 |
22 |
36 |
|
41 |
53 |
60 |
23 |
37 |
|
30 |
54 |
46 |
24 |
36 |
|
37 |
53 |
58 |
25 |
36 |
|
31 |
54 |
42 |
26 |
35 |
|
45 |
53 |
57 |
27 |
36 |
|
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53 |
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|
|
Mike realized he also needed input from Ann about her perspective on the business. She indicated that she felt strongly that with better management, Northcutt Bikes should be able to use the existing warehouse for years to come, even with the anticipated growth in business. Currently, however, she views the situation as a crisis because “we’re bursting at the seams with inventory. It’s costing us a lot of profit, yet our service level is very poor, at less than 80%. I’d like to see us maintain a 95% or better service level without back orders, yet we need to be able to do that with a net reduction in total inventory. What do you think, Mike? Can we do better?”
|
FB378 |
|
GS131 |
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Actual |
Actual |
Week |
ForecastDemand |
ForecastDemand |
|
1 |
30 |
34 |
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2 |
32 |
44 |
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3 |
35 |
33 |
|
4 |
34 |
39 |
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5 |
35 |
48 |
|
Questions
1. Use the available data to develop inventory policies (order quantities and reorder points) for the FB378 and GS131. Assume that the holding cost is 20% of unit price.
2. Compare the inventory costs associated with your sug-gested order quantities with those of the current order quantities. What can you conclude?
3. Do you think the lost customer sales should be included as a cost of inventory? How would such an inclusion impact the ordering policies you established in question 1?
Books and Articles
Blackstone, J. H., ed., APICS Dictionary, 14th ed. (Chicago, IL: APICS, 2013).
Lee, H. L., V. Padmanabhan, and S. Whang, “The Bullwhip Ef-fect in Supply Chain,” Sloan Management Review 38, no. 3 (Spring 1997): 70–77.
Magretta, J., “Fast, Global, and Entrepreneurial: Supply Chain Management, Hong Kong Style,” Harvard Business Review 76, no. 5 (September–October 1998): 102–109.
Magretta, J., “The Power of Virtual Integration: An Interview with Dell Computer’s Michael Dell,” Harvard Business Review 76, no. 2 (March–April 1998): 72–84.
Paul A. Souders/Corbis
Chapter Outline
Introduction
12.1 Master Scheduling
12.2 Material Requirements Planning
12.3 Production Activity Control and Vendor Order Management Systems
12.4 Synchronizing Planning and Control across the Supply Chain
Chapter Summary
Managing Production across the Supply Chain
Chapter ObjeCtives
By the end of this chapter, you will be able to:
· Complete the calculations for the master schedule record and interpret the results.
· Complete the calculations for the MRP record and interpret the results.
· Discuss the role of production activity control and vendor order management and how these functions differ from higher-level planning activities.
· Explain how distribution requirements planning (DRP) helps synchronize the supply chain and complete the calculations for a simple example.
358
CHAPTER 12 • Managing Production across the Supply Chain 359
Bigdawg Customs, Part 1
Supertrooper/Shutterstock |
Steve Barr, owner of BigDawg Customs, still wasn’t sure whether making the new KZ1 scooter seat was a good business move or a bad one. On the one hand, tapping into the scooter market had definitely boosted sales for
BigDawg, which had traditionally made motorcycle ac-cessories. The company was selling over 1,500 KZ1 seats each month and demand was growing. On the other hand, production planning and control for the KZ1 seat was a mess. BigDawg only produced the KZ1 scooter seat in large batches every few weeks, but there were parts inventories all over the place, even in the weeks when BigDawg was not
making seats. What was even more worrisome to Steve was that no one had a good handle on how many of the seats being made had already been sold to someone. More than once, a BigDawg salesperson had promised to ship seats to a large customer, only to find out that the inventory in the warehouse was spoken for. Steve worried that if this kept up, his customers would take their business elsewhere. Steve realized it was time to have a meeting with two of his key managers, Theresa Griggs, vice president of marketing, and Brad Asbaugh, vice president of production.
“Folks, as you know the KZ1 has been a big success in the marketplace, and I can see us offering more scooter seats over the next year. I appreciate your efforts to get us to this point, but we’ve got some issues to resolve. First, we need some way to match up production with the actual orders we have coming in. We just can’t tell customers that we will ship them something in a cou-ple of weeks—we have to know when seats are available and when we can ship them. Also, it seems to me that we can do a better job managing our parts inventories. We have stacks of saddles, hardware kits, and covers out on the plant floor, but we aren’t going to be making an-other batch of seats for another week. There’s got to be a way to do it. Let’s meet again next week and you can show me what you’ve got.”
Planning and control
A set of tactical and execution-level business activities that includes master scheduling, material requirements plan-ning, and some form of pro-duction activity control and vendor order management.
The purpose of this chapter is to introduce you to some of the systems manufacturers use to manage production and to coordinate these activities with their supply chain partners. While the focus here is on physical goods, bear in mind that many service firms also depend on the infor-mation generated by these efforts. For instance, distributors and transportation carriers all use information generated by manufacturers to plan their own activities.
Planning and control can be thought of as a set of tactical and execution-level business processes that include master scheduling, material requirements planning, and some form of production activity control and vendor order management. Planning and control begins where sales and operations planning (S&OP) ends, as Figure 12.1 shows. The first step in planning and control is master scheduling, in which the overall resource levels established by S&OP begin to be fleshed out with specifics. The master schedule states exactly when and in what quantities specific products will be made. It also links production with specific customer orders, allow-ing the firm to tell the customer exactly when an order will be filled. Finally, master schedul-ing informs the operations manager what inventory or resources are still available to meet new demand. As we shall see, the concept of available to promise is an important function of master scheduling.
Material requirements planning (MRP) takes the process one step further: It translates the master schedule for final products into detailed material requirements. For example, if the master schedule indicates that 500 chairs will be finished and ready to sell in week 5, MRP de-termines when the individual pieces—seats, legs, back spindles, and so on—need to be made or purchased.
At the lowest level in the hierarchy are two systems: production activity control (PAC) and vendor order management. At this point, all the plans have been made; the primary task
360 PART IV • Planning and Controlling Operations and Supply Chains
Figure 12.1
A Top-Down Model of
Manufacturing Planning and
Control Systems
Sales and operations planning (S&OP)
Master scheduling
Material requirements planning (MRP)
Establishes overall production, workforce, and inventory levels (tactical capacity planning)
Determines when speci c products will be made, when speci c customer orders will be lled, and what products/capacities are still available to meet new demand
Calculates the timing and quantities of material orders needed to support
the master schedule
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Production activity |
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Vendor |
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control (PAC) |
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order management |
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Assures that in-house |
Assures that materials ordered |
|||
manufacturing takes place |
from supply chain partners |
||||
according to plan; also helps |
are received when needed; |
||||
manufacturing managers identify |
also helps purchasing managers |
||||
potential problems and |
identify potential problems and |
||||
|
take corrective actions |
|
take corrective actions |
remaining is to make sure they are executed properly. Because materials ultimately come either from in-house manufacturing or from outside suppliers, two distinct types of control systems have sprung up to handle those different environments.
Our description of planning and control seems to suggest a top-down process, with higher-level plans feeding into more detailed lower-level systems. Why, then, do the arrows in Figure 12.1 run in both directions? The reason is simple: Changes in the business environment or other conditions may become apparent at lower levels, requiring the organization to adjust its plans and actions in real time.
In the rest of this chapter, we describe planning and control tools in more detail, start-ing with master scheduling and ending with PAC and vendor order management systems. We also discuss distribution requirements planning (DRP), one tool for synchronizing planning and control across the supply chain. As thorough as this chapter is, it cannot begin to cover all the choices firms face in designing their planning and control systems. Our intent, rather, is to give you an appreciation of both the advantages and the effort needed to run these systems.
Master scheduling
A detailed planning process that tracks production output and matches this output to actual customer orders.
Master scheduling is a detailed planning process that tracks production output and matches this output to actual customer orders. We have already said that master scheduling picks up where S&OP leaves off. Figure 12.2 gives an example of this linkage. The top of the figure shows four months of a sales and operations plan for a fictional manufacturer of lawn equipment. Note that management has established overall targets for demand, production, and ending inventory. These targets will guide the firm’s tactical decisions, including planned workforce levels, storage space requirements, and cash flow needs. The bottom half of the figure shows the monthly mas-ter schedules for the three products the company produces. For every week in March, it shows what the expected demand is, how many of each product will be produced, and what the pro-jected ending inventory is.
If we add up the numbers for production and demand across the three master schedules, we see that they match the figures in the sales and operations plan. Similarly, if we add up the ending inventory figures in week 4 of the master schedules, we see that they, too, match the figures in the plan. As long as the sales and operations planning values (for instance, the num-ber of labor hours required per unit) are correct, the company should have enough capacity to
Figure 12.2
The Link between the Sales and Operations Plan and the Master Schedule
CHAPTER 12 • Managing Production across the Supply Chain 361
Partial sales and operations plan |
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||
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Ending |
Month |
Demand |
Production Inventory |
|
January |
1,500 |
1,500 |
700 |
February |
2,500 |
2,500 |
700 |
March |
4,000 |
5,000 |
1,700 |
April |
5,000 |
6,000 |
2,700 |
Master schedules for March |
|
Week 1 Week 2 Week 3 Week 4 |
||||
Push mowers |
Demand |
|
200 |
250 |
300 |
350 |
|
Production |
|
650 |
0 |
650 |
0 |
|
Ending inventory |
200 |
650 |
400 |
750 |
400 |
|
Demand |
|
400 |
500 |
600 |
700 |
Power mowers |
Production |
|
0 |
1,350 |
0 |
1350 |
|
Ending inventory |
400 |
0 |
850 |
250 |
900 |
|
Demand |
|
100 |
150 |
200 |
250 |
Lawn tractors |
Production |
|
250 |
250 |
250 |
250 |
|
Ending inventory |
100 |
250 |
350 |
400 |
400 |
Beginning inventory = |
700 |
Total monthly production = |
15,000 |
Total monthly demand = |
24,000 |
Ending inventory = |
1,700 |
implement these master schedules. In reality, however, the demand and production numbers in the master schedule are unlikely to match the sales and operations plan exactly. Furthermore, the actual capacity requirements might not match the planning values. For example, the plan may state that the average product needs an estimated 4.5 hours of labor, but the actual figure may turn out to be 4.7 hours. In such cases, firms may need to dip into their safety stock, sched-ule overtime, or take other measures to make up the difference between the plan and reality. As long as the numbers in the sales and operations plan are close to those in the master schedule, firms will be able to manage the differences.
Now that we understand the linkage between the sales and operations plan and the master schedule, let’s look at the master schedule record in more detail. Because firms tailor the master schedule record to their manufacturing environment and the characteristics of their product, generalizing about its precise form is difficult. Nevertheless, most master schedule records track several key pieces of information:
· Forecasted demand;
· Booked orders;
· Projected inventory levels;
· Production quantities; and
· Units still available to meet customer needs (available to promise).
To illustrate how the master schedule works, let’s look at the master schedule record for
Sandy-Built, a company that makes snowblowers (Figure 12.3).
Forecasted Demand versus Booked Orders. At the beginning of November (week 45), Sandy-Built’s management is reviewing the master schedule for the company’s newest model,
362 PART IV • Planning and Controlling Operations and Supply Chains
Figure 12.3
Partial Master Schedule Record for the MeltoMatic Snowblower
Forecasted demand
In the context of master scheduling, a company’s best estimate of the demand in any period.
Booked orders
In the context of master scheduling, confirmed demand for products.
Master production schedule (MPS)
The amount of product that will be finished and available for sale at the beginning of each week. The master pro-duction schedule drives more detailed planning activities, such as material requirements planning.
Projected ending inventory
A field in the master schedule record that indicates estimated inventory level at the end of each time period.
Figure 12.4
Partial Master Schedule Record for the MeltoMatic Snowblower
MeltoMatic snowblower |
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Month |
************November************ |
************December************ |
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Week |
45 |
46 |
47 |
48 |
49 |
50 |
|
51 |
52 |
Forecasted demand |
150 |
150 |
150 |
150 |
175 |
175 |
175 |
|
175 |
Booked orders |
170 |
165 |
140 |
120 |
85 |
42 |
20 |
|
0 |
Master production schedule |
300 |
0 |
300 |
0 |
350 |
0 |
350 |
|
0 |
the MeltoMatic. The master schedule record in Figure 12.3 shows the forecasted demand— the company’s best estimate of the demand in any period—for the months of November and December. It also shows booked orders, which represent confirmed demand for products. At this point, forecasted demand is running behind booked orders. In week 45, for instance, the estimated demand for snowblowers is 150, yet Sandy-Built already has confirmed orders for 170.
Now look at the forecasts and booked orders for December. In that month, booked orders appear to be lagging behind forecasted demand. Perhaps more orders will materialize as Decem-ber draws nearer. But if booked orders do not increase, managers may need to take action, either by cutting back production or by lowering the price of the MeltoMatic to move more units. One of the benefits of master scheduling is that it allows managers to take corrective action when needed.
Another line on the master schedule record, called the master production schedule (MPS), shows how many products will be finished and available for sale at the beginning of each week. In our example, Sandy-Built seems to be producing enough snowblowers every other week to meet the forecasted demand.
Ending Inventory. With the basic numbers we have so far, we start to get a picture of what over-all inventory levels should look like and, more importantly, how many more snowblowers we can sell. Figure 12.4 contains a new row called projected ending inventory, which is simply our best estimate of what inventory levels will look like at the end of each week, based on current information.
Projected ending inventory is calculated as follows:
|
EIt = EIt-1 + MPSt - maximum (Ft, OBt) |
(12.1) |
where: |
= ending inventory in time period t |
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EIt |
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MPSt |
= master production schedule quantity available in time period t |
|
Ft |
= forecasted demand for time period t |
|
OBt |
= orders booked for time period t |
|
Note that projected ending inventory is a conservative estimate of the inventory position at the end of each week. In our example, the inventory at the end of week 44 is 100. Therefore, the projected inventory at the end of week 45 is 100 + 300 - 170 = 230, and the same calculation for week 46 is 230 + 0 - 165 = 65. In each case, we use booked orders because this number is higher than the forecasted demand. This makes sense because using the lower forecasted de-
mand numbers would overestimate inventory levels.
But what about other weeks, such as week 47, in which the forecasted demand is higher than booked orders? In this case, the assumption is that the booked orders (140) probably do not reflect all the demand that will eventually occur in that week (150). To be conservative, we subtract the higher number in calculating ending inventory: 65 + 300 - 150 = 215.
On-hand inventory at end of week 44 |
100 |
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MeltoMatic snowblower |
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Month |
************November************ |
************December************ |
||||||||
Week |
45 |
46 |
47 |
48 |
49 |
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50 |
|
51 |
52 |
Forecasted demand |
150 |
150 |
150 |
150 |
175 |
175 |
|
175 |
|
175 |
Booked orders |
170 |
165 |
140 |
120 |
85 |
42 |
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20 |
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0 |
Projected ending inventory |
230 |
65 |
215 |
65 |
240 |
65 |
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240 |
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65 |
Master production schedule |
300 |
0 |
300 |
0 |
350 |
0 |
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350 |
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0 |
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CHAPTER 12 • Managing Production across the Supply Chain 363 |
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Figure 12.5 |
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Calculating Available to |
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Promise (ATP) for Week 45 |
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Week 46 Orders |
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Week 45 Orders |
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100 |
300 |
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Snowblowers |
Snowblowers |
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Current |
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Additional units nished |
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Inventory left |
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Current |
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Total: 165 |
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over from |
and available to ship |
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Total: 170 |
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week 44 |
in week 45 |
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400 Snowblowers |
– |
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335 Snowblowers |
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= 65 Snowblowers |
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Available to promise (ATP)
A field in the master schedule record that indicates the num-ber of units that are available for sale each week, given those that have already been prom-ised to customers.
Available to Promise. Now suppose you work for Sandy-Built’s sales department and it is the beginning of week 45. You have the information shown in Figure 12.4 sitting in front of you. A customer calls and asks how many snowblowers you can sell to him at the beginning of week 45 and at the beginning of week 47. To answer this question, you need to know how many snowblowers are available to promise. Available to promise (ATP) indicates the number of units that are available for sale each week, given those that have already been promised to customers.
To illustrate how ATP is calculated, consider Figure 12.5, which represents MeltoMatic’s master schedule at the beginning of week 45. On the supply side, there are 100 snowblowers left over from the previous week. Another 300 snowblowers are scheduled to be finished in week 45. As a result, there will be a total supply of 400 snowblowers. On the demand side, Sandy- Built has already booked orders for 170 and 165 snowblowers in weeks 45 and 46, respectively. (We need to consider orders through week 46 because no new snowblowers are expected to be com-pleted until week 47.) When we take the difference between the supply (400) and the demand 170 + 165 = 335 shown in Figure 12.5, we get a value of 65. This figure represents the num-ber of additional units we can sell—that is, available to promise—until the next MPS quantity comes in.
Figure 12.5 tells us the available to promise quantity for the next two weeks, but what about for week 47, which corresponds with the next MPS quantity? Figure 12.6 shows the logic. Since week 47 is still two weeks away (remember, we’re in the beginning of week 45), we can’t be sure how many snowblowers will be left over from week 46. Therefore, the only supply we can count on is the 300 units being completed in week 47. On the demand side, whatever supply we have in week 47 must carry us through weeks 47 and 48. Total booked orders for these weeks equals 140 + 120 = 260. Therefore, the available to promise for week 47 is 300 - 260 = 40 snowblowers.
Figure 12.6 |
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Calculating Available to |
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Promise (ATP) for Week 47 |
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Week 48 Orders |
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Week 47 Orders |
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300 |
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Snowblowers |
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Inventory left |
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Current |
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Total: 120 |
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over from |
and available to ship |
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Total: 140 |
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week 46 |
in week 47 |
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364 PART IV • Planning and Controlling Operations and Supply Chains
Now that you understand the logic behind ATP, let’s state it more formally. The formula for ATP for the first week of the master schedule record is:
z-1
ATPt = EIt-1 + MPSt - AOBi
i=t
For any subsequent week in which MPS 7 0, it is:
z-1
ATPt = MPSt - AOBi
i=t
where:
ATPt = available to promise in week t EIt-1 = ending inventory in week t – 1
MPSt = master production schedule quantity in week t
(12.2)
(12.3)
z-1 |
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AOBi |
= sum of all orders booked from week t until week z (when the next positive |
i=t |
MPS quantity is due) |
Because week 45 is the first week of the master schedule record, we use Equation (12.2) to calculate the available-to-promise numbers:
z-1
ATPt = EIt-1 + MPSt - AOBi
i=t
46
ATP45 = EI44 + MPS45 - A OBi
i=45
= 100 + 300 - 170 + 165 = 65 snowblowers
Note that an ATP number must always be calculated for the first week in the record, re-gardless of whether any units are finished that week. Look at Figure 12.7. The ATP calculation for week 47 follows Equation (12.3), which assumes that there is no holdover inventory:
z-1
ATPt = MPSt - AOBi
i=t
48
ATP47 = MPS47 - A OBi
i=47
= 300 - 140 + 120 = 40 snowblowers
Looking at it another way, total booked orders for November are 170 + 165 + 140 + 120 = 595 snowblowers, while the total units that we can sell are 100 + 300 + 300 = 700. The difference between these two totals is 700 - 595 = 105 snowblowers: 65 in the first two weeks of November and 40 in the last two weeks.
To summarize, Equation (12.2) is used to calculate the ATP for the first week of the master schedule record; Equation (12.3) is used for subsequent periods in which the MPS is positive. In calculating the ATP, managers must look ahead to see how many periods will go by before the next batch of finished products is ready.
On-hand inventory at end of week 44 |
100 |
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************November************ |
************December************ |
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Week |
45 |
46 |
47 |
48 |
49 |
50 |
51 |
52 |
Forecasted demand |
150 |
150 |
150 |
150 |
175 |
175 |
175 |
175 |
Booked orders |
170 |
165 |
140 |
120 |
85 |
42 |
20 |
0 |
Projected ending inventory |
230 |
65 |
215 |
65 |
240 |
65 |
240 |
65 |
Master production schedule |
300 |
0 |
300 |
0 |
350 |
0 |
350 |
0 |
Available to promise |
65 |
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40 |
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223 |
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330 |
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Figure 12.7 Complete Master Schedule Record for the MeltoMatic Snowblower
CHAPTER 12 • Managing Production across the Supply Chain 365
EXAMPLE 12.1
Completing the Master
Schedule Record for
Karam’s Alpine Hiking
Gear
Galyna Andrushko/Shutterstock |
Lisa Karam is the owner of Karam’s Alpine Hiking Gear. Lisa has set up the following master schedule record for one of her most popular products, the Eiger1 backpack. She needs to complete the projected ending inventory and available-to-promise calculations (Figure 12.8).
Using Equation (12.1), the projected ending inventory values for weeks 37 through 39 are calculated as follows:
EIt |
= EIt - 1 |
+ MPSt - maximum Ft, OBt |
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EI37 |
= 2,000 |
+ |
0 |
- maximum |
1500, 1422 |
= 500 backpacks |
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EI38 |
= |
500 + 4,500 - maximum 1500, 1505 = 3,495 backpacks |
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EI39 |
= |
3,495 |
+ |
0 |
- maximum |
1500, 1471 |
= 1,995 backpacks |
The remaining projected ending inventory values are calculated in a similar fashion. The master schedule record will also have four ATP calculations: one for the first week (week 37) and one for each week in which the MPS is positive (weeks 38, 41, and 44):
ATP37 |
= 2,000 |
+ 0 - 1,422 = 578 backpacks |
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ATP38 |
= 4,500 |
- |
1,505 + 1,471 + 1,260 = 264 backpacks |
ATP41 |
= 4,000 |
- |
980 + 853 + 534 = 1,633 backpacks |
ATP44 |
= 3,700 |
- 209 = 3,491 backpacks |
Figure 12.8
Incomplete Master
Production Schedule
Record for Eiger1
Backpack
The completed master schedule record is shown in Figure 12.9. Interpreting the results, Lisa would expect the inventory to drop no lower than about 500 backpacks (week 37).
In addition, Lisa has 578 backpacks left to sell in the current week. If she enters week 38 with no inventory, she will have only an additional 264 backpacks to sell over the following three weeks. Because of this, Lisa might try to get customers who aren’t in a hurry to book orders in October, when the ATP quantities are much higher.
On-hand inventory at end of week 36 |
2,000 |
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Eiger1 backpack |
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Month |
************September************ |
*************October************* |
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Week |
37 |
38 |
39 |
40 |
41 |
42 |
43 |
44 |
Forecasted demand |
1,500 |
1,500 |
1,500 |
1,400 |
1,400 |
1,250 |
1,250 |
1,250 |
Booked orders |
1,422 |
1,505 |
1,471 |
1,260 |
980 |
853 |
534 |
209 |
Projected ending inventory |
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Master production schedule |
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Available to promise |
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366 PART IV • Planning and Controlling Operations and Supply Chains
Figure 12.9
Completed Master
Production Schedule Record
for Eiger1 Backpack
On-hand inventory at end of week 36 |
2,000 |
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Eiger1 backpack |
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Month |
************September************ |
*************October************* |
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Week |
37 |
38 |
39 |
40 |
41 |
42 |
43 |
44 |
Forecasted demand |
1,500 |
1,500 |
1,500 |
1,400 |
1,400 |
1,250 |
1,250 |
1,250 |
Booked orders |
1,422 |
1,505 |
1,471 |
1,260 |
980 |
853 |
534 |
209 |
Projected ending inventory |
500 |
3,495 |
1,995 |
595 |
3,195 |
1,945 |
695 |
3,145 |
Master production schedule |
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4,500 |
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4,000 |
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3,700 |
Available to promise |
578 |
264 |
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1,633 |
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3,491 |
Planning horizon
The amount of time the master schedule record or MRP record extends into the future. In gen-eral, the longer the production and supplier lead times, the longer the planning horizon must be.
The Planning Horizon. The master schedule records we have shown so far happen to extend eight weeks into the future. In reality, the appropriate planning horizon will depend on the lead time a firm needs to source parts and build a product. Products with very short lead times may have planning horizons that are just a few weeks long, but more complex products may need horizons of several months or more.
As the weeks go by, a firm will need to revise the numbers in the master schedule record, a task that is referred to as “rolling through” the planning horizon. For example, for the Melto-Matic snowblower, the current week in Figure 12.7 is week 45. At the end of week 45, the master schedule record will roll forward, and the new current week will be week 46.
Figure 12.10
Updated Master Schedule Record for the MeltoMatic Snowblower
We have shown how to calculate the master schedule numbers, but how do real firms use the results of these calculations? Look again at Figure 12.7. Imagine that Sandy-Built receives a call from a large retail chain that the company has never dealt with before. The buyer needs 150 snowblowers “as soon as possible.” Sandy-Built would like to do business with this customer, but management had not anticipated such a huge order. When can Sandy-Built ship the snowblow-ers, and what will be the impact on production?
With a formal master schedule, managers can quickly answer these questions. According to the ATP figures in Figure 12.7, Sandy-Built can ship 65 snowblowers now, 40 more in week 47, and the remaining 45 in week 49 65 + 40 + 45 = 150 . If Sandy-Built decides to accept this order, however, managers will need to recalculate the ending inventory and ATP numbers. Figure 12.10 shows the updated master schedule record.
Booked orders in weeks 45, 47, and 49 are now 235, 180, and 130. Because the new order is so large, projected ending inventories drop dramatically. In fact, the calculations suggest that inventories will drop to zero on a regular basis unless management alters production levels to increase the safety stock. Finally, the retailer’s large order will use up all the ATP for November. Unless another order is canceled, Sandy-Built cannot accept new orders until December—a change the sales force should be made aware of.
The master schedule calculations might seem complicated at first, but imagine what could go wrong if a business did not have this information available. Salespeople wouldn’t be sure if and when they could fill customer orders. Production managers might not become aware of the impact of new demand on inventory levels in time to do something about it. Worse still, sales-people might continue to promise products to customers, unaware that all output has already been spoken for. In short, chaos would result. When master scheduling works well, it allows
On-hand inventory at end of week 44 |
100 |
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MeltoMatic snowblower |
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Month |
************November************ |
************December************ |
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Week |
45 |
46 |
47 |
48 |
49 |
50 |
51 |
52 |
Forecasted demand |
150 |
150 |
150 |
150 |
175 |
175 |
175 |
175 |
Booked orders |
235 |
165 |
180 |
120 |
130 |
42 |
20 |
0 |
Projected ending inventory |
165 |
0 |
120 |
0 |
175 |
0 |
175 |
0 |
Master production schedule |
300 |
0 |
300 |
0 |
350 |
0 |
350 |
0 |
Available to promise |
0 |
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178 |
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330 |
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CHAPTER 12 • Managing Production across the Supply Chain 367 |
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EXAMPLE 12.2 |
After completing the master schedule record in Figure 12.9, Lisa receives a call from a hik- |
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Booking More Orders at |
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ing outfitter in Montana. The customer would like Lisa to send 50 of the Eiger1 backpacks |
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Karam’s Alpine Hiking |
in the third week of September (week 39). Can Lisa do it? Lisa updates the master schedule |
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Gear |
record to reflect the change. The results are shown in Figure 12.11. |
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When Lisa compares the updated master schedule record to the old one in Figure 12.9, |
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she sees that booking the new order increases orders booked in week 39 by 50 backpacks |
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and reduces the ATP for week 38 by 50. The projected ending inventory for week 39 also |
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falls but not by 50 backpacks, as one might expect. Rather, it falls by just 21 backpacks—the |
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difference between new orders booked and forecasted demand (1,521 1,500). |
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On-hand inventory at end of week 36 |
2,000 |
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Eiger1 backpack |
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Month |
************September************ |
*************October************* |
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Week |
37 |
38 |
39 |
40 |
41 |
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42 |
43 |
44 |
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Forecasted demand |
1,500 |
1,500 |
1,500 |
1,400 |
1,400 |
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1,250 |
1,250 |
1,250 |
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Booked orders |
1,422 |
1,505 |
1,521 |
1,260 |
980 |
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853 |
534 |
209 |
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Projected ending inventory |
500 |
3,495 |
1,974 |
574 |
3,174 |
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674 |
3,124 |
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Master production schedule |
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4,500 |
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4,000 |
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3,700 |
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Available to promise |
578 |
214 |
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1,633 |
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3,491 |
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Figure 12.11 Updated Master Production Schedule Record for Eiger1 Backpack
organizations to avoid these problems by closely matching demand with supply, anticipating customers’ needs, and adjusting the organization’s plans accordingly.
12.2 Material Requirements Planning
Rough-cut capacity planning
A capacity planning technique that uses the master produc-tion schedule to monitor key resource requirements.
Material requirements planning (MRP)
A planning process that trans-lates the master production schedule into planned orders for the actual parts and com-ponents needed to produce the master schedule items.
Dependent demand inventory
Inventory items whose demand levels are tied directly to the production of another item.
With strategic capacity planning (Chapter 6), S&OP (Chapter 10), and master scheduling, we have a comprehensive set of high-level planning tools. Master scheduling, as we have seen, is particularly valuable because it allows managers to match production figures to actual customer demand. In addition, some firms use the master production schedule to monitor key resource requirements, an activity called rough-cut capacity planning. For instance, Sandy-Built’s man-agers, seeing that 350 snowblowers are scheduled to be completed in week 49, might check to make sure the company has the capacity to meet that production goal. Rough-cut capacity plan-ning verifies the feasibility of the master schedule.
Material requirements planning, more commonly known as MRP, takes planning one step further by translating the master production schedule into planned orders for the actual parts and components needed to produce the master schedule items. The logic of the MRP approach to inventory management is completely different from the independent inventory approaches described in Chapter 11. This is because MRP is used to manage dependent demand inventory, or inventory items whose demand levels are tied directly to the production of another item. Suppose, for instance, that each MeltoMatic snowblower Sandy -Built produces requires three wheels. Once managers know how many snowblowers they are going to make, they can calculate exactly how many wheels they will need and when they will need them. The demand for wheels is completely dependent on the number of snowblowers made. Unlike independent demand items, then, there is no mystery about how many dependent demand items a firm will need and when. MRP takes advantage of this fact to manage inventory quite differently—and more efficiently—than an EOQ-based system.
MRP is based on three related concepts:
1. The bill of material (BOM);
2. Backward scheduling; and
3. Explosion of the bill of material.
We will illustrate these concepts using a simple example, the assembly of a furniture piece called the King Philip chair.
368 PART IV • Planning and Controlling Operations and Supply Chains
EXAMPLE 12.3
The Bill of Material
(BOM) for the King
Philip Chair
Bill of material (BOM)
According to APICS, “a listing of all the subassemblies, interme-diates, parts, and raw materials that go into a parent assembly showing the quantity of each required to make an assembly.”
The bill of material (BOM) is “a listing of all the subassemblies, intermediates, parts, and raw materials that go into a parent assembly, showing the quantity of each required to make an assembly.”1 The bill of material for the King Philip chair has 10 different compo-nents, shown in Figure 12.12.
Seat
Back slats (3)
Front legs (2) Side rails (2) Crossbars (2)
Product structure tree
A record or graphical rendering that shows how the components in the BOM are put together to make the level 0 item.
Figure 12.12 Bill of Material (BOM) for the King Philip Chair
The product structure tree in Figure 12.13 shows how the components in the BOM are put together to make the chair. The chair is assembled using a leg assembly, a back assembly, and a seat; the leg and back assemblies, in turn, are assembled from individual components such as legs, back slats, and crossbars. In MRP jargon, the complete chair is a level 0 item; the leg assembly, back assembly, and seat are level 1 items; and the remaining components are level 2 items. In practice, product assemblies can be dozens of levels deep.
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FigurE 12.13 Product Structure Tree for the King Philip Chair |
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The product structure tree also shows the planning lead time for each component. |
Planning lead time |
The planning lead time is the time from when a component or material is ordered until |
In the context of MRP, the time |
it arrives and is ready to use. For instance, the finished chair has a planning lead time of |
from when a component is |
one week, the amount of time workers need to assemble a typical batch of chairs using the |
ordered until it arrives and is |
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level 1 items. Seats have a planning lead time of two weeks, which may reflect the time an |
ready to use. |
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detail later in this chapter. |
EXAMPLE 12.4 |
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We can now show how backward scheduling (exploding the BOM) is used in MRP. The |
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Backward Scheduling |
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master schedule record in Figure 12.14 shows that 500 finished chairs should be ready to |
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(Exploding the BOM) |
sell at the beginning of week 5. How do managers ensure that this commitment is met? |
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for the King Philip Chair |
To complete manufacture of 500 chairs by the beginning of week 5, workers must start |
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assembling the chairs at the beginning of week 4. (Recall from Figure 12.13 that the plan- |
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ning lead time for the assembled chair is one week.) This deadline can be met only if the |
1Definition of Bill of Material in J. H. Blackstone, ed., APICS Dictionary, 14th ed. (Chicago, IL: APICS, 2013). Reprinted by permission.
Exploding the BOM
The process of working backward from the master production schedule for a level 0 item to determine the quantity and timing of orders for the various subassemblies and components. Exploding the BOM is the underlying logic used by MRP.
CHAPTER 12 • Managing Production across the Supply Chain 369
On-hand inventory at end of December |
600 |
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King Philip chair |
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Month |
************January************ |
************February************ |
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Week |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
Forecasted demand |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
Booked orders |
100 |
90 |
85 |
80 |
70 |
85 |
80 |
90 |
Projected ending inventory |
500 |
400 |
300 |
200 |
600 |
500 |
400 |
300 |
Master production schedule |
0 |
0 |
0 |
0 |
500 |
0 |
0 |
0 |
Available to promise |
245 |
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175 |
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Figure 12.14 Master Schedule Record for the King Philip Chair
back assemblies, leg assemblies, and seats are available at the beginning of week 4. Con-tinuing to work backward in time, we see that workers must start the back and leg as-semblies at the beginning of week 3 in order to have them ready by the beginning of week 4. Seats have a two-week lead time, so they must be ordered no later than the beginning of week 2. Back slats, crossbars, side rails, and legs must be scheduled at the beginning of week 1—right now!—if managers want to have 500 chairs ready to go in week 5.
The time line in Figure 12.15 shows the logic behind backward scheduling. From a single order for 500 chairs in week 5, we worked backward, first through the level 1 items and then through the level 2 items. This process is called exploding the BOM.
Week 1 |
Week 2 |
Week 3 |
Week 4 |
Week 5• |
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Figure 12.15 Exploding the BOM for the King Philip Chair
The simple MRP record builds on the backward scheduling logic but provides some additional information. Like the master schedule record, the format of the MRP record may differ slightly from one firm to the next, but the basic principle—working backward from the planned comple-tion date for the final item—is the same.
Figure 12.16 shows an example of how the MRP record is calculated. Looking at point A in the top row of Figure 12.16, we see that management has committed to having 500 chairs ready at the beginning of week 5. Given the planning lead time from Figure 12.13, workers need to start assembling the chairs in week 4 (point B). This assembly task triggers the need for level 1 components such as seats.
The bottom half of Figure 12.16 shows the MRP record for the seat. The top row shows gross requirements—that is, how many seats are needed each week. Because no chairs are being assembled in weeks 1 through 3, the gross requirement for seats in those weeks is zero (point C). In week 4, the gross requirement for seats is 500 (point D). This number is drawn directly from the “Start assembly” quantity at point B.
Gross requirements can be met by drawing from three sources: inventory carried over from the previous week, or the projected ending inventory; units already on order, referred to
370 PART IV • Planning and Controlling Operations and Supply Chains
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WEEK |
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**King Philip chair** |
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1 |
2 |
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4 |
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7 |
LT (weeks) = 1 |
MPS due date |
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0 |
0 |
0 |
0 |
A 500 |
400 |
300 |
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Start assembly |
0 |
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0 |
B 500 |
400 |
300 |
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C |
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***Seat*** |
Gross requirements |
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0 |
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0 |
D 500 |
400 |
300 |
0 |
LT (weeks) = 2 |
Scheduled receipts |
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0 |
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Projected ending inventory |
0 |
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0H |
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Net requirements |
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E 500 |
400 |
300 |
0 |
Min. order = 1 |
Planned receipts |
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F 500 |
400 |
300 |
0 |
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Planned orders |
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0 |
G 500 |
400 |
300 |
0 |
0 |
0 |
Figure 12.16 Calculating the MRP Record for Seats (King Philip Chair)
as scheduled receipts; and new orders, termed planned receipts. To determine whether any new orders need to be placed, we must first calculate net requirements:
NRt = maximum 0; GRt - EIt - 1 - SRt |
(12.4) |
where:
NRt = net requirement in time period t
GRt = gross requirement in time period t
EIt - 1 = ending inventory from time period t – 1
SRt = scheduled receipts in time period t
In lay terms, if enough seats can be obtained from inventory and scheduled receipts to cover the gross requirements, then managers don’t need to order any more seats (i.e., the net requirement equals zero). Otherwise, they have a net requirement that must be met with new planned receipts.
In our chair example, the projected inventory at the end of week 3 is zero, and there are no scheduled receipts in week 4. Therefore, the net requirement for seats in week 4 is:
NR4 = maximum |
0; GR4 - EI3 - SR4 |
= maximum |
0; 500 - 0 - 0 = 500 |
This result is shown in Figure 12.16 as point E. If you look in the lower-left corner of Figure 12.16, you will see that the minimum order size for seats is 1. In general, a business would not want to order more units than necessary as doing so would increase inventory levels and costs. Therefore, managers should plan on ordering just enough seats to meet the net require-ment (point F). If they plan to receive 500 seats in week 4, they must release the order no later than week 2 (point G) because of the two-week planning lead time for seats. Finally, the ending inventory for week 4 (point H) is calculated using Equation (12.5):
EIt = EIt - 1 + SRt + PRt - GRt |
(12.5) |
where:
EIt = ending inventory from time period t
EIt - 1 = ending inventory from time period t – 1
SRt = scheduled receipts in time period t
PRt = planned receipts in time period t
GRt = gross requirements in time period t
EI4 = EI3 + SR4 + PR4 - GR4
= 0 + 0 + 500 - 500 = 0 seats
To test your understanding of the MRP record, try tracing the calculations through weeks 5 and 6. Figure 12.17 shows the complete MRP record for all the level 1 items, including the leg assembly and the back assembly. The logic behind the calculations is the same, but a couple of things should be noted. First, the factory begins week 1 with 25 leg assemblies in inventory (point I). Because there are no gross requirements in the first three weeks, these assemblies gather dust until they are needed in week 4. Though the net requirement in week 4 is only 475,
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CHAPTER 12 • Managing Production across the Supply Chain |
371 |
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WEEK |
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**King Philip chair** |
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LT (weeks) = 1 |
MPS due date |
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500 |
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300 |
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Start assembly |
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300 |
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7 |
***Seat*** |
Gross requirements |
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500 |
400 |
300 |
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LT (weeks) = 2 |
Scheduled receipts |
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Projected ending inventory |
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Net requirements |
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500 |
400 |
300 |
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Min. order = 1 |
Planned receipts |
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500 |
400 |
300 |
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Planned orders |
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300 |
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***Leg asm*** |
Gross requirements |
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0 |
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500 |
400 |
300 |
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LT (weeks) = 1 |
Scheduled receipts |
I |
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Projected ending inventory |
25 |
25 |
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525 |
125 |
825 |
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825 |
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Net requirements |
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475 |
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175 |
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Min. order = 1,000 |
Planned receipts |
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1,000 |
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1,000 |
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Planned orders |
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1,000 |
J |
0 K 1,000 |
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***Back asm*** |
Gross requirements |
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500 |
400 |
300 |
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LT (weeks) = 1 |
Scheduled receipts |
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250 L |
0 |
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Projected ending inventory |
0 |
250 |
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250 |
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Net requirements |
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250 |
400 |
300 |
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Min. order = 250 |
Planned receipts |
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250 |
400 |
300 |
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Planned orders |
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0 |
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250 |
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400 |
300 |
0 |
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0 |
Figure 12.17 MRP Records for the Level 1 Components
managers place an order for 1,000 (point J) because that is the minimum order size. The result is excess inventory at the end of week 4.
In week 5, the factory has more than enough leg assemblies (525) in beginning inventory to meet the gross requirement (400). As a result, managers do not place any additional orders (point K). Finally, for the back assemblies, the factory has a scheduled receipt of 250 units in week 1 (point L). These units will sit in inventory until week 4, when they are needed.
Just as the gross requirements for level 1 items are determined by the number of finished chairs (level 0) to be manufactured, the gross requirements for level 2 items depend on the planned orders for level 1 items.
Figure 12.18 shows the complete MRP calculations for all components in the King Philip chair. Notice that managers want to put together 1,000 leg assemblies in week 3 (planned orders = 1,000). Because each leg assembly requires two legs (Figure 12.13), the gross requirement for legs in week 3 is 2,000 (point M). Similarly, each back assembly requires two side rails. There-fore, a planned order for 300 back assemblies in week 5 results in a gross requirement of 600 side rails in the same week (point N).
Now for a real test. Where do the crossbar’s gross requirements in Figure 12.18 come from? Because the crossbar is used in two different level 1 items, we must calculate gross require-ments based on planned orders for both the leg assemblies and the back assemblies. Therefore:
Gross requirements for crossbars = leg assembly planned orders
+ back assembly planned orders
Week 3 : 1,000 + 250 = 1,250
Week 4: 0 + 400 = 400
Week 5 : 1,000 + 300 = 1,300
Once we have calculated the gross requirements, filling out the rest of the MRP records is a matter of following the rules outlined earlier.
372 PART IV • Planning and Controlling Operations and Supply Chains |
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Figure 12.18 |
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WEEK |
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Complete MRP Records for |
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
the King Philip Chair |
** Chair kit** |
MPS due date |
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500 |
400 |
300 |
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LT (weeks) = 1 |
Start assembly |
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500 |
400 |
300 |
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MyOMLab Animation |
** Seat ** |
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Gross requirements |
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500 |
400 |
300 |
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LT (weeks) = 2 |
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Scheduled receipts |
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Projected ending inventory |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
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Min. order = 1 |
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Net requirements |
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500 |
400 |
300 |
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Planned receipts |
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500 |
400 |
300 |
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Planned orders |
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500 |
400 |
300 |
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** Leg asm ** |
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Gross requirements |
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500 |
400 |
300 |
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LT (weeks) = 1 |
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Scheduled receipts |
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Projected ending inventory |
25 |
25 |
25 |
25 |
525 |
125 |
825 |
825 |
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Net requirements |
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475 |
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175 |
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Min. order = 1,000 |
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Planned receipts |
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1,000 |
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1,000 |
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Planned orders |
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1,000 |
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1,000 |
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** Back asm ** |
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Gross requirements |
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500 |
400 |
300 |
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LT (weeks) = 1 |
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Scheduled receipts |
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250 |
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Projected ending inventory |
0 |
250 |
250 |
250 |
0 |
0 |
0 |
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Min. order = 250 |
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Net requirements |
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M |
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250 |
400 |
300 |
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Planned receipts |
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250 |
400 |
300 |
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Planned orders |
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250 |
400 |
300 |
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** Legs ** |
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Gross requirements |
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2,000 |
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2,000 |
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N |
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LT (weeks) = 2 |
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Scheduled receipts |
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Projected ending inventory |
25 |
25 |
25 |
0 |
0 |
0 |
0 |
0 |
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Net requirements |
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1,975 |
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2,000 |
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Min. order = 1 |
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Planned receipts |
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1,975 |
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2,000 |
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Planned orders |
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1,975 |
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2,000 |
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** Side rails ** |
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Gross requirements |
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500 |
800 |
600 |
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LT (weeks) = 2 |
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Scheduled receipts |
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500 |
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Projected ending inventory 100 |
600 |
600 |
100 |
0 |
0 |
0 |
0 |
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Min. order = 500 |
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Net requirements |
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700 |
600 |
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Planned receipts |
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700 |
600 |
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Planned orders |
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700 |
600 |
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** Back slats ** |
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Gross requirements |
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750 |
1,200 |
900 |
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LT (weeks) = 2 |
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Scheduled receipts |
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75 |
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Projected ending inventory |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
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Min. order = 1 |
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Net requirements |
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750 |
1,125 |
900 |
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Planned receipts |
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750 |
1,125 |
900 |
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Planned orders |
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750 |
1,125 |
900 |
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** Crossbars ** |
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Gross requirements |
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1,250 |
400 |
1,300 |
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LT (weeks) = 2 |
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Scheduled receipts |
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Projected ending inventory |
0 |
0 |
0 |
0 |
600 |
300 |
300 |
300 |
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Min. order = 1,000 |
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Net requirements |
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1,250 |
400 |
700 |
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Planned receipts |
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1,250 |
1,000 |
1,000 |
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Planned orders |
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1,250 |
1,000 |
1,000 |
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EXAMPLE 12.5
Using MRP at Karam’s
Alpine Hiking Gear
The BOM and associated planning lead times for the Eiger1 backpack are shown in Figure 12.19.
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Eiger1 backpack |
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0 weeks* |
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Soft |
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Frame |
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1 week |
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Shoulder straps |
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Belt |
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1 week |
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bag |
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Buckle |
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frame |
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frame |
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1 week |
1 week |
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1 week |
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2 weeks |
*To save on shipping and assembly costs, the Eiger1 backpack is sold unassembled. The dealer takes the Level 1 components and puts them together at the shop.
Figure 12.19 BOM for the Eiger1 Backpack
CHAPTER 12 • Managing Production across the Supply Chain 373
Lisa Karam has asked you to set up the MRP records for all the components for the next six weeks. Lisa also tells you the following:
· According to the master production schedule, Karam is planning on having 850 new backpacks ready to sell at the beginning of each of weeks 4, 5, and 6.
· Currently, there is no component inventory of any kind in the plant.
· The soft bag, shoulder straps, and belt straps all have minimum order quantities of 1,500 units. All of the other components have no minimum order quantity.
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WEEK |
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1 |
2 |
3 |
4 |
5 |
6 |
*Eiger1 packs* |
MPS due date |
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850 |
850 |
850 |
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LT (weeks) = 0 |
Start assembly |
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850 |
850 |
850 |
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** Soft bag ** |
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Gross requirements |
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0 |
0 |
0 |
850 |
850 |
850 |
LT (weeks) = 2 |
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Scheduled receipts |
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Projected ending inventory |
0 |
0 |
0 |
0 |
650 |
1,300 |
450 |
Min. order = 1,500 |
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Net requirements |
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850 |
200 |
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Planned receipts |
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1,500 |
1,500 |
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Planned orders |
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1,500 |
1,500 |
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** Frame ** |
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Gross requirements |
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0 |
0 |
0 |
850 |
850 |
850 |
LT (weeks) = 1 |
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Scheduled receipts |
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Projected ending inventory |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Min. order = 1 |
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Net requirements |
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850 |
850 |
850 |
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Planned receipts |
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850 |
850 |
850 |
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Planned orders |
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850 |
850 |
850 |
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** Shoulder straps ** |
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Gross requirements |
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0 |
0 |
0 |
1,700 |
1,700 |
1,700 |
LT (weeks) = 1 |
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Scheduled receipts |
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|
|
|
|
|
|
|
|
Projected ending inventory |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Min. order = 1,500 |
|
Net requirements |
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|
|
|
1,700 |
1,700 |
1,700 |
|
|
Planned receipts |
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|
1,700 |
1,700 |
1,700 |
|
|
Planned orders |
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|
1,700 |
1,700 |
1,700 |
|
** Belt ** |
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|
Gross requirements |
|
0 |
0 |
0 |
850 |
850 |
850 |
LT (weeks) = 1 |
|
Scheduled receipts |
|
|
|
|
|
|
|
|
|
Projected ending inventory |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Min. order = 1 |
|
Net requirements |
|
|
|
|
850 |
850 |
850 |
|
|
Planned receipts |
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|
|
|
850 |
850 |
850 |
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Planned orders |
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|
|
850 |
850 |
850 |
|
** Left frame ** |
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|
Gross requirements |
|
0 |
0 |
850 |
850 |
850 |
0 |
LT (weeks) = 1 |
|
Scheduled receipts |
|
50 |
|
|
|
|
|
|
|
Projected ending inventory |
0 |
50 |
50 |
0 |
0 |
0 |
0 |
Min. order = 1 |
|
Net requirements |
|
|
|
800 |
850 |
850 |
|
|
|
Planned receipts |
|
|
|
800 |
850 |
850 |
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|
Planned orders |
|
|
800 |
850 |
850 |
|
|
** Right frame ** |
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|
Gross requirements |
|
0 |
0 |
850 |
850 |
850 |
0 |
LT (weeks) = 1 |
|
Scheduled receipts |
|
|
|
|
|
|
|
|
|
Projected ending inventory |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Min. order = 1 |
|
Net requirements |
|
|
|
850 |
850 |
850 |
|
|
|
Planned receipts |
|
|
|
850 |
850 |
850 |
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Planned orders |
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|
850 |
850 |
850 |
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|
** Buckle ** |
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|
Gross requirements |
|
0 |
0 |
850 |
850 |
850 |
0 |
LT (weeks) = 1 |
|
Scheduled receipts |
|
|
|
|
|
|
|
|
|
Projected ending inventory |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Min. order = 1 |
|
Net requirements |
|
|
|
850 |
850 |
850 |
|
|
|
Planned receipts |
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|
850 |
850 |
850 |
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Planned orders |
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|
850 |
850 |
850 |
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|
* Belt straps * |
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|
Gross requirements |
|
0 |
0 |
1,700 |
1,700 |
1,700 |
0 |
LT (weeks) = 2 |
|
Scheduled receipts |
|
|
|
|
|
|
|
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|
Projected ending inventory |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Min. order = 1,500 |
|
Net requirements |
|
|
|
1,700 |
1,700 |
1,700 |
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|
Planned receipts |
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|
1,700 |
1,700 |
1,700 |
|
|
|
Planned orders |
|
1,700 |
1,700 |
1,700 |
|
|
|
Figure 12.20 MRP Records for the Eiger1 Backpack
374 PART IV • Planning and Controlling Operations and Supply Chains
· At present, the only scheduled receipt is for 50 left frames in week 1 (the result of an earlier partial shipment on the part of a vendor).
The completed MRP records are shown in Figure 12.20.
There are a couple of interesting points to note:
1. In the current week, the only action that needs to be taken is to release an order for 1,700 belt straps.
2. Because the Eiger1 backpacks do not have to be assembled, the final assembly plan-ning lead time is zero.
3. The gross requirements for the shoulder straps are twice those of any other level 1 item. This is because each backpack requires two shoulder straps.
4. The MRP record for the left frame is nearly identical to that for the right frame. The difference is due to the 50 “extra” left frames arriving in week 1. These extra left frames reduce the planned order release in week 2 by 50 units.
Parent/child relationship
The logical linkage between higher- and lower-level items in the BOM.
Just as in master scheduling, getting lost in the calculations is easy to do with MRP. Figures 12.18 and 12.20 showed all the MRP records for two very simple products. Imagine what the MRP records must look like in a firm that produces hundreds of products, with dozens of BOM levels and thousands of components!
So now is a good time to pull back and consider the benefits of MRP:
1. MRP is directly tied to the master production schedule and indicates the exact timing and quantity of orders for all components. By eliminating a lot of the guesswork associ-ated with the management of dependent demand inventory, MRP simultaneously low-ers inventory levels and helps firms meet their master schedule commitments.
2. MRP allows managers to trace every order for lower-level items through all the lev-els of the BOM, up to the master production schedule. This logical linkage between higher and lower levels in the BOM is sometimes called the parent/child relationship. If for some reason the supply of a lower-level item is interrupted, a manager can quickly check the BOM to see the impact of the shortage on production.
3. MRP tells a firm and its suppliers precisely what needs to be made when. This informa-tion can be invaluable in scheduling work or shipments, or even in planning budgets and cash flows. In fact, MRP logic is often called the “engine” of planning and control systems. MRP plays a big part in many enterprise resource planning (ERP) systems, described in the supplement.
The complexity of MRP demands that these systems be computerized. But even with the help of computers, MRP requires organizational discipline. Like the calendar function on your cell phone, MRP provides little benefit to those who do not understand and exploit the system.
For an MRP system to work properly, it must have accurate information. Key data include the master production schedule, the BOM, inventory levels, and planning lead times. If any of this information is inaccurate, components will not be ordered at the right time or in the right quantities. In some cases, the correct components won’t be ordered at all. As a result, most firms that want to implement MRP find that they must first ensure accurate planning information.
MRP systems must also accommodate uncertainty about a host of factors, including the possibility of variable lead times, shipment quantities and quality levels, and even changes to the quantities in the master production schedule. In general, firms deal with this uncertainty by lengthening the planning lead times or by holding additional units as safety stock. Of course, such buffers increase the amount of inventory in the system. As a result, many firms make a con-scious effort to eliminate uncertainty. They do so by choosing suppliers and processes that offer
|
CHAPTER 12 • Managing Production across the Supply Chain 375 |
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reliable lead times and high quality levels and by keeping the quantities on the master produc- |
MRP nervousness |
tion schedule firm. Reducing uncertainty requires a high degree of organizational discipline, but |
A term used to refer to the |
the rewards can be great. |
observation that any change, |
A final consideration in implementing an MRP system is a phenomenon called MRP ner- |
even a small one, in the re- |
vousness. Because higher-level items drive the requirements for lower-level items in an MRP |
quirements for items at the top |
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system, any change, even a small one, in the requirements for upper-level items can have drastic |
of the bill of material can have |
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effects on items listed further down the bill of material. Example 12.6 shows how such changes |
drastic effects on items further |
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down the bill of material. |
can affect the MRP records. |
example 12.6 |
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After completing the MRP records for the King Philip chair (Figure 12.18), management |
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MRP Nervousness for |
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decides to change the number of chairs to be completed in week 7 from 300 to 125. Fig- |
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the King Philip Chair |
ure 12.21 shows the impact of this change on the MRP records. As you can see, no MRP |
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record is left untouched. |
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WEEK |
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
|
** Chair kit** |
MPS due date |
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|
500 |
400 |
125 |
||
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LT (weeks) = 1 |
Start assembly |
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500 |
400 |
125 |
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** Seat ** |
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|
Gross requirements |
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|
500 |
400 |
125 |
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LT (weeks) = 2 |
|
Scheduled receipts |
|
|
|
|
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|
Projected ending inventory |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
|
|
Min. order = 1 |
|
Net requirements |
|
|
|
|
500 |
400 |
125 |
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|
Planned receipts |
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|
500 |
400 |
125 |
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Planned orders |
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|
500 |
400 |
125 |
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** Leg asm ** |
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Gross requirements |
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|
500 |
400 |
125 |
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|
LT (weeks) = 1 |
|
Scheduled receipts |
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Projected ending inventory |
25 |
25 |
25 |
25 |
525 |
125 |
0 |
0 |
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|
Min. order = 1,000 |
|
Net requirements |
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|
475 |
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Planned receipts |
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|
1,000 |
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Planned orders |
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1,000 |
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** Back asm ** |
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Gross requirements |
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|
500 |
400 |
125 |
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LT (weeks) = 1 |
|
Scheduled receipts |
|
250 |
0 |
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Projected ending inventory |
0 |
250 |
250 |
250 |
0 |
0 |
0 |
0 |
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|
Min. order = 250 |
|
Net requirements |
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|
250 |
400 |
125 |
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Planned receipts |
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|
250 |
400 |
125 |
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Planned orders |
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250 |
400 |
125 |
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** Legs ** |
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Gross requirements |
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2,000 |
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LT (weeks) = 2 |
|
Scheduled receipts |
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Projected ending inventory |
25 |
25 |
25 |
0 |
0 |
0 |
0 |
0 |
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Min. order = 1 |
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Net requirements |
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1,975 |
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Planned receipts |
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1,975 |
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Planned orders |
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1,975 |
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** Side rails ** |
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Gross requirements |
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|
500 |
800 |
250 |
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LT (weeks) = 2 |
|
Scheduled receipts |
|
500 |
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Projected ending inventory 100 |
600 |
600 |
100 |
0 |
250 |
250 |
250 |
||
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Min. order = 500 |
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Net requirements |
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700 |
250 |
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Planned receipts |
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700 |
500 |
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Planned orders |
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700 |
500 |
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** Back slats ** |
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Gross requirements |
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750 |
1,200 |
375 |
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LT (weeks) = 2 |
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Scheduled receipts |
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75 |
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Projected ending inventory |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
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Min. order = 1 |
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Net requirements |
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750 |
1,125 |
375 |
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Planned receipts |
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750 |
1,125 |
375 |
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Planned orders |
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750 |
1,125 |
375 |
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** Crossbars ** |
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Gross requirements |
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1,250 |
400 |
125 |
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LT (weeks) = 2 |
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Scheduled receipts |
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Projected ending inventory |
0 |
0 |
0 |
0 |
600 |
475 |
475 |
475 |
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Min. order = 1,000 |
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Net requirements |
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1,250 |
400 |
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Planned receipts |
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1,250 |
1,000 |
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Planned orders |
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1,250 |
1,000 |
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Figure 12.21 MRP Nervousness for the King Philip Chair
376 PART IV • Planning and Controlling Operations and Supply Chains
Compared to Figure 12.18, the change eliminates the need for a second planned order of 1,000 leg assemblies in week 5. This, in turn, affects the gross requirements for legs and crossbars. The change in planned production also spills over to the records for seats, back assemblies, side rails, and back slats, although the impact is not quite as pronounced.
The point is that a minor change at the top can cause huge changes at lower levels. Planners must take MRP nervousness into consideration when making changes, especially with higher-level items. They must also choose their minimum order quantities with care. Notice the impact of the minimum order, or lot size, for leg assemblies: The firm went from ordering 1,000 leg assemblies in week 5 to ordering none at all that week. Because large lot sizes make MRP systems more nervous, firms that take this approach to inventory man-agement usually try to keep their minimum order quantities as small as possible, especially for higher-level items that have the potential to disrupt lower-level requirements.
12.3 Production Activity Control and Vendor Order Management Systems
To this point, we have been discussing planning tools: S&OP for planning overall resource levels, master scheduling for planning the production and shipment of end items, and MRP for plan-ning orders for manufacturing components. With production activity control (PAC) and vendor order management systems, the emphasis shifts from planning to execution. Besides their many other capabilities, these systems can:
1. Route and prioritize jobs going through the supply chain;
2. Coordinate the flow of goods and materials between a facility and other supply chain partners; and
3. Provide supply chain partners with performance data on operations and supply chain activities.
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The tools and techniques used to perform PAC and vendor order management are as varied as |
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Job sequencing rules |
the operational environments in which they are used. They can be as simple as the rules for de- |
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ciding which manufacturing job should be processed next or as complex as high-tech software or |
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Rules used to determine the |
hardware solutions for tracking the flow of materials among supply chain partners. Job sequenc- |
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order in which jobs should be |
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ing rules have been used for decades to determine the order in which jobs should be processed |
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processed when resources are |
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limited and multiple jobs are |
when resources are limited and multiple jobs are waiting to be done. And as Example 12.7 shows, |
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waiting to be done. |
job sequencing is just as valid in a services environment as it is in manufacturing. |
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EXAMPLE 12.7 |
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Carlos’s Restoration Services restores antique paintings. The process consists of three steps. For |
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Job Sequencing at |
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each painting, the first step must be completed prior to the second, and the second step must be |
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Carlos’s Restoration |
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completed prior to the third. Furthermore, Carlos’s can work on only one job at a time at each step. |
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Services |
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Carlos’s has four jobs waiting to be started. Information on these jobs, shown in the |
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order in which they arrived, is contained in Table 12.1. |
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Table 12.1 Job Requirements for Carlos’s Restoration Services |
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Estimated Days |
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Total |
Days |
Critical |
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Job |
Step 1 |
Step 2 |
Step 3 |
Task Time |
Until Due |
Ratio |
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Uptown Gallery |
3 |
2 |
3.5 |
8.5 |
21 |
2.47 |
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High Museum |
5 |
2 |
1 |
8 |
20 |
2.50 |
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Chester College |
3 |
2 |
5 |
10 |
10 |
1.00 |
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Smith |
6 |
4 |
1 |
11 |
15 |
1.36 |
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CHAPTER 12 • Managing Production across the Supply Chain 377
Total task times range from 8 to 11 days. Chester College has requested that its job be completed in 10 days, while Uptown Gallery is willing to wait 21 days. One way to deter-mine the order in which jobs should be sequenced is based on the critical ratio. The critical ratio is calculated as follows:
Critical ratio = |
days until due |
(12.6) |
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total task time remaining |
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A critical ratio of 1 indicates that the amount of task time equals the amount of time left; hence, any time spent waiting will make the job late. A critical ratio less than 1 in-dicates that the job is going to be late unless something changes. When the critical ra-tio is used to sequence work, the jobs with the lowest critical ratio are scheduled to go first. Carlos’s decides to test three common job sequencing rules—first come, first served (FCFS), earliest due date (EDD), and the critical ratio—to see which one performs best. The results are shown in Table 12.2.
Table 12.2 Testing Three Common Job Sequencing Rules at Carlos’s Restoration Services
First come, first served |
Step 1 |
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Step 2 |
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Step 3 |
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Job |
Start |
End |
Start |
End |
Start |
End |
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Days Late |
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Uptown Gallery |
0 |
3 |
3 |
5 |
5 |
8.5 |
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0 |
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High Museum |
3 |
8 |
8 |
10 |
10 |
11 |
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0 |
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Chester College |
8 |
11 |
11 |
13 |
13 |
18 |
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8 |
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Smith |
11 |
17 |
17 |
21 |
21 |
22 |
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7 |
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Average lateness: |
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3.75 |
days |
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Earliest due date |
Step 1 |
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Step 2 |
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Step 3 |
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Job |
Start |
End |
Start |
End |
Start |
End |
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Days Late |
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Chester College |
0 |
3 |
3 |
5 |
5 |
10 |
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0 |
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Smith |
3 |
9 |
9 |
13 |
13 |
14 |
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0 |
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High Museum |
9 |
14 |
14 |
16 |
16 |
17 |
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0 |
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Uptown Gallery |
14 |
17 |
17 |
19 |
19 |
22.5 |
1.5 |
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Average lateness: |
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0.375 |
days |
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Critical ratio |
Step 1 |
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Step 2 |
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Step 3 |
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Job |
Start |
End |
Start |
End |
Start |
End |
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Days Late |
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Chester College |
0 |
3 |
3 |
5 |
5 |
10 |
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0 |
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Smith |
3 |
9 |
9 |
13 |
13 |
14 |
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0 |
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Uptown Gallery |
9 |
12 |
13 |
15 |
15 |
18.5 |
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0 |
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High Museum |
12 |
17 |
17 |
19 |
19 |
20 |
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0 |
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Average lateness: |
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0 |
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Processing the jobs on a first-come, first-served basis might seem the fairest, but in this case, the result is that two jobs are finished long before they’re due, while two jobs are consid-erably late. Sequencing the jobs according to the earliest due date results in somewhat better results: Only the Uptown Gallery job is late (1.5 days), for an average lateness of 0.375 days.
Carlos’s then sequences the jobs from highest to lowest critical ratio value. In this case, all the jobs are completed prior to the due date. Based on these results, Carlos’s decides to use the critical ratio to set the sequence.
Monitoring and Tracking Technologies
Radio-frequency identification (RFID), bar coding, and online order tracking systems have been developed to trace the movement and location of materials in the supply chain and report on the progress of specific jobs. Such systems depend on computer hardware and software that can in-terpret the information gathered by the system. Herman Miller, a designer and manufacturer of
378 PART IV • Planning and Controlling Operations and Supply Chains
high-end office furniture, incorporates PAC and vendor order management tools. Besides help-ing the company to control its operations and supply chain activities, these systems also alert managers to potential problems. For example, computer displays located throughout Herman Miller’s plant provide users with real -time information about the status of manufacturing jobs and required materials. If a shortage of materials threatens to delay a job, the system flags the problem and indicates which jobs will be affected. Managers at Herman Miller or at supply chain partners’ facilities can then take corrective action.2
12.4 Synchronizing Planning and Control Across the Supply Chain
Throughout this book, we have emphasized the need to synchronize decisions across the supply chain. This need is especially critical in planning and control activities. In this section, we introduce one technique for synchronizing planning and control decisions: distribution requirements planning (DRP). In Chapter 13, we will talk about another technique, called kanban. DRP helps to synchronize supply chain partners at the master schedule level, while kanban systems help to synchronize them at the PAC and vendor order management levels (Figure 12.22).
Distribution requirements planning (DRP)
A time-phased planning ap-proach similar to MRP that uses planned orders at the point
of demand (customer, ware-house, etc.) to determine fore-casted demand at the source level (often a plant).
Distribution Requirements Planning
Distribution requirements planning (DRP) is a time-phased planning approach similar to MRP that uses planned orders at the point of demand (customer, warehouse, etc.) to determine forecasted demand at the source level (often a plant). DRP is one of many ways in which supply chain partners can synchronize their planning efforts at the master schedule level. These fore-casted demand numbers then become input to the master scheduling process.
To illustrate how DRP works, let’s return to the example of Sandy- Built’s MeltoMatic snowblower. When you first looked at the master schedule record shown in Figure 12.7, you may have wondered where the forecasted demand numbers came from. After all, much of the value of master scheduling hinges on the accuracy of forecasts. Managers typically base their forecasts on past history or educated guesses, but DRP forecasts are calculated directly from
Figure 12.22
Synchronized Planning and
Control
Sales and operations planning (S&OP)
Master scheduling |
DRP |
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Material requirements planning (MRP)
Production |
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Kanban |
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order management |
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activity control (PAC) |
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systems |
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2Bundy, W. “Miller SQA: Leveraging Technology for Speed and Reliability,” Supply Chain Management Review 3, no. 2
(Spring 1999): 62–69.
CHAPTER 12 • Managing Production across the Supply Chain 379
Figure 12.23 Minneapolis distribution
Downstream Supply Chain center
for MeltoMatic Snowblowers
Retailers
Buffalo distribution
center
Manufacturing
downstream supply partners’ requirements. That is, DRP uses MRP-style logic to feed accurate demand information into the master schedule.
Suppose the MeltoMatic is sold through two regional distribution centers, one in Minneapolis, Minnesota, and the other in Buffalo, New York. These distribution centers, in turn, sell directly to retailers. Figure 12.23 shows the structure of this downstream supply chain.
Each distribution center has its own weekly demand forecasts, inventory data, order lead times, and minimum order quantities. Both centers use this information to estimate when they will need to place orders with the main plant.
The two sections at the top of Figure 12.24 show the DRP records for the two distribution centers. Note that these records are almost identical to MRP records, with one exception: Instead of gross requirements, they show forecasted demand. Here, the term forecasted demand refers to the number of snowblowers each center expects to ship to retail customers each week. By substitut-ing forecasted demand for gross requirements, managers at the distribution centers can calculate net requirements, planned receipts, and planned orders. Finally, activities at these two distribution centers are synchronized when their total weekly planned orders become forecasted demand in the factory’s master schedule (see the third section of Figure 12.24). Master scheduling occurs as usual, except that the forecasted demand is tied explicitly to planned orders at the distribution centers.
Now look at what happens when forecasted demand changes at the distribution centers (Figure 12.25). Starting in week 49, the forecasted demand at the Minneapolis distribution center has increased dramatically. What is the impact of this change on the master schedule? Logic suggests that in order to meet the increased demand, Sandy-Built’s managers will need to
Minneapolis distribution center |
Month |
********November******** |
********December******** |
********January******** |
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Week |
45 |
46 |
47 |
48 |
49 |
50 |
51 |
52 |
1 |
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4 |
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Forecasted demand |
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60 |
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90 |
15 |
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75 |
75 |
Min. order = 120 |
Net requirements |
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30 |
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45 |
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48 |
49 |
50 |
51 |
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15 |
Min. order = 100 |
Net requirements |
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Week |
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46 |
47 |
48 |
49 |
50 |
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52 |
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Forecasted demand |
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100 |
220 |
100 |
120 |
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100 |
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Projected ending inventory |
37 |
257 |
37 |
157 |
37 |
137 |
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257 |
37 |
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Master production schedule |
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320 |
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220 |
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0 |
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Available to promise |
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257 |
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220 |
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320 |
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440 |
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Figure 12.24 DRP Records for the MeltoMatic Snowblower
380 PART IV • Planning and Controlling Operations and Supply Chains
Minneapolis distribution center |
Month |
********November******** |
********December******** |
********January******** |
||||||||||
|
Week |
45 |
46 |
47 |
48 |
49 |
50 |
51 |
52 |
1 |
2 |
3 |
4 |
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LT (weeks) = 2 |
Forecasted demand |
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60 |
60 |
60 |
60 |
90 |
90 |
90 |
90 |
110 |
110 |
130 |
130 |
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Scheduled receipts |
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120 |
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0 |
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Projected ending inventory |
75 |
15 |
75 |
15 |
75 |
105 |
15 |
45 |
75 |
85 |
95 |
85 |
75 |
Min. order = 120 |
Net requirements |
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0 |
0 |
0 |
45 |
15 |
0 |
75 |
45 |
35 |
25 |
35 |
45 |
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Planned receipts |
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120 |
120 |
0 |
120 |
120 |
120 |
120 |
120 |
120 |
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Planned orders |
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Buffalo distribution center |
Month |
********November******** |
********December******** |
********January******** |
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Week |
45 |
46 |
47 |
48 |
49 |
50 |
51 |
52 |
1 |
2 |
3 |
4 |
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LT (weeks) = 1 |
Forecasted demand |
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80 |
80 |
85 |
85 |
90 |
90 |
95 |
95 |
100 |
100 |
105 |
105 |
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Scheduled receipts |
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100 |
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Projected ending inventory |
25 |
45 |
65 |
80 |
95 |
5 |
15 |
20 |
25 |
25 |
25 |
20 |
15 |
Min. order = 100 |
Net requirements |
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0 |
35 |
20 |
5 |
0 |
85 |
80 |
75 |
75 |
75 |
80 |
85 |
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Planned receipts |
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0 |
100 |
100 |
100 |
0 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
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Planned orders |
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100 |
100 |
100 |
0 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
0 |
Master schedule, |
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Month |
********November******** |
********December******** |
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MeltoMatic snowblowers |
Week |
45 |
46 |
47 |
48 |
49 |
50 |
51 |
52 |
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Forecasted demand |
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100 |
220 |
220 |
0 |
220 |
220 |
220 |
220 |
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Booked orders |
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100 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
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Projected ending inventory |
37 |
257 |
37 |
37 |
37 |
257 |
37 |
257 |
37 |
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Master production schedule |
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320 |
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220 |
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440 |
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440 |
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Available to promise |
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257 |
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220 |
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440 |
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440 |
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Figure 12.25 The Impact of Forecast Changes on DRP Records
increase the master production schedule to 440 snowblowers in week 49. The point is that DRP quickly translates downstream demand into upstream production decisions.
Figure 12.26 provides a final, high-level view of how DRP helps synchronize Sandy-Built’s supply chain. Retailer orders drive not only Sandy-Built’s plans but also those of upstream sup-pliers who plan their activity based on Sandy- Built’s material orders. In effect, every MPS quan-tity or MRP planned order can be traced back to demand from the retailers.
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Retailers’ actual orders . . . |
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Figure 12.26 Synchronizing Plans across the Supply Chain
CHAPTER 12 • Managing Production across the Supply Chain 381
This chapter has provided a comprehensive overview of the various tools companies use to manage production, starting with master scheduling, then MRP and job sequencing, and ending with DRP. Planning and control systems aid manufacturers and service firms alike by helping them to determine the quantities and timing of their activities. Put another way, production man-agement should be of interest not only to manufacturing firms but to virtually all firms involved in the flow of physical products.
Today, advances in information technology are radically changing planning and control systems in two fundamental ways. First, faster computers and extensive communications
networks are expanding the depth and breadth of planning and control activities. Firms can replan and share new information with their supply chain partners almost instantaneously. Sec-ond, planning and control software tools are becoming more sophisticated. Some firms even have advanced decision sup-port tools that allow them to quickly evaluate multiple plans or even to generate an optimal plan.
That said, the usefulness of planning and control systems still depends on people who understand how they work and how to use them correctly. This fundamental requirement will
never change.
EXAMPLE 12.8
BigDawg Customs
Revisited
“Ok, so what should we do?” asked Steve Barr, owner of BigDawg Customs.
Theresa Griggs, vice president of marketing, spoke up. “One of the problems you mentioned was that currently we really didn’t have a way to match up production of KZ1 seats with actual customer orders. So I’ve worked with Brad in manufacturing to develop a master production schedule for the KZ1. Brad, show Steve how it works.”
Brad Ashbaugh handed Steve a preliminary master schedule for the KZ1 (Fig- HYPERLINK \l "page398" ure 12.27). “The master schedule really does several things. First, it allows us to compare the weekly forecasts Theresa has developed against our planned production levels. Second, it allows us to …”
KZ1 inventory at end of March = |
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Month |
***************April*************** |
***************May*************** |
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Week |
14 |
15 |
16 |
17 |
18 |
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19 |
20 |
21 |
Forecast demand |
300 |
300 |
400 |
450 |
500 |
500 |
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500 |
500 |
Orders booked |
240 |
295 |
170 |
150 |
90 |
0 |
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0 |
0 |
Projected ending inventory |
–48 |
852 |
452 |
2 |
1,002 |
502 |
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1,002 |
Master production schedule |
0 |
1,200 |
0 |
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1,500 |
0 |
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1,500 |
Available-to-promise |
12 |
585 |
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1,410 |
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1,500 |
Figure 12.27 Master Schedule for KZ1 Scooter Seat
“Woah!” Steve Barr interjected. “What does that negative inventory number in Week 14 mean? Have we overpromised again?”
Brad replied, “Good question. No, we haven’t overpromised yet. In fact, the ‘available-to-promise’ line for Week 14 tells us we still have another 12 seats that we can sell this week before we make more seats next week. The –48 means that, given what we’ve already promised and what we have available, we expect that we’ll have to turn away orders for 48 seats this week. This is not great and we want to avoid this in the future, but not meeting demand is still better than promising something we can’t deliver.”
“Ok, I think I get it. So how will this master schedule work as time goes on?” asked Steve. Theresa answered, “Every week, Brad and I will get together and roll the schedule forward one week. We will update the forecast numbers and see what adjustments if any should be made to the master production schedule. Also—and this is key—before a sales-person makes a sale, they will first need to check to see whether inventory is available, and then make sure the master schedule is updated every time there is a change to the orders booked. This way we can make sure we don’t promise something we can’t deliver.”
Steve replied, “OK, that sounds good, but what about our parts inventory prob-lem?” Brad spoke up, “The KZ1 seat is assembled from three components we order from vendors: the saddle, the cover, and a hardware kit. We can use material requirements planning to tell us when we need to order stuff so that we don’t order too earlier and we
382 PART IV • Planning and Controlling Operations and Supply Chains
don’t order too much. Brad then handed Steve a preliminary copy of the MRP for the KZ1 seat (Figure 12.28).
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WEEK |
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***KZ1 Seat*** |
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14 |
15 |
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17 |
18 |
19 |
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21 |
LT (weeks) = 1 |
MPS due date |
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0 |
1,200 |
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1,500 |
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15 |
16 |
17 |
18 |
19 |
20 |
21 |
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Gross requirements |
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1,200 |
0 |
0 |
1,500 |
0 |
0 |
1,500 |
0 |
LT (weeks) = 2 |
Scheduled receipts |
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0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
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Projected ending inventory |
1,650 |
450 |
450 |
450 |
0 |
0 |
0 |
0 |
0 |
$30 |
Net requirements |
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0 |
0 |
0 |
1,050 |
0 |
0 |
1,500 |
0 |
|
Planned receipts |
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0 |
0 |
0 |
1,050 |
0 |
0 |
1,500 |
0 |
Min. order = 1 |
Planned orders |
0 |
0 |
1,050 |
0 |
0 |
1,500 |
0 |
0 |
0 |
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WEEK |
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14 |
15 |
16 |
17 |
18 |
19 |
20 |
21 |
***Hardware Kit*** |
Gross requirements |
|
1,200 |
0 |
0 |
1,500 |
0 |
0 |
1,500 |
0 |
LT (weeks) = 1 |
Scheduled receipts |
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0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
$20 |
Projected ending inventory |
2,200 |
1,000 |
1,000 |
1,000 |
500 |
500 |
500 |
0 |
0 |
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Net requirements |
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0 |
0 |
0 |
500 |
0 |
0 |
1,000 |
0 |
Min. order = 1,000 |
Planned receipts |
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0 |
0 |
0 |
1,000 |
0 |
0 |
1,000 |
0 |
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Planned orders |
0 |
0 |
0 |
1,000 |
0 |
0 |
1,000 |
0 |
0 |
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WEEK |
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14 |
15 |
16 |
17 |
18 |
19 |
20 |
21 |
***Cover*** |
Gross requirements |
|
1,200 |
0 |
0 |
1,500 |
0 |
0 |
1,500 |
0 |
LT (weeks) = 2 |
Scheduled receipts |
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0 |
250 |
0 |
0 |
0 |
0 |
0 |
0 |
$10 |
Projected ending inventory |
1,200 |
0 |
250 |
250 |
0 |
0 |
0 |
0 |
0 |
|
Net requirements |
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0 |
0 |
0 |
1,250 |
0 |
0 |
1,500 |
0 |
Min. order = 250 |
Planned receipts |
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0 |
0 |
0 |
1,250 |
0 |
0 |
1,500 |
0 |
|
Planned orders |
0 |
0 |
1,250 |
0 |
0 |
1,500 |
0 |
0 |
0 |
Figure 12.28 MRP Record for KZ1 Scooter Seat
Brad continued, “We are about to go into Week 14 of the year, and we currently have 1,650 saddles, 2,200 hardware kits, and 1,200 covers sitting in inventory. At current costs, this inventory is worth:
1,650 saddles * +30 each + 2,200 hardware kits * +20 each
+ +1,200 * +10 each = +105,500”
“Ugh! And most of that stuff has been sitting around for a couple weeks,” interjected Steve. “Exactly,” Brad continues, “but if we use MRP to plan the timing and quantities of orders, we can reduce component inventories to zero by the end of Week 21.”
Steve, Theresa, and Brad discuss the master schedule and MRP records for a while longer until Steve is satisfied and has a basic understanding of how the planning tools work. Finally, he says:
“This looks like a really good start, and I’ll be interested to see how this works in prac-tice. I guess I have a couple of questions. First, what do we need to do to help make sure everyone follows the rules—that is, placing customer orders through the master schedule, and keeping accurate inventory records? Also, once we get this system working for the KZ1 seats, how might we apply it to other areas of our business?”
CHAPTER 12 • Managing Production across the Supply Chain 383
Projected ending inventory for the master schedule record (page 362):
|
EIt = EIt-1 + MPSt - maximum Ft, OBt |
(12.1) |
where: |
= ending inventory in time period t |
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EIt |
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MPSt |
= master production schedule quantity available in time period t |
|
Ft |
= forecasted demand for time period t |
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OBt |
= orders booked for time period t |
|
Available to promise for the master schedule record (page 364):
For the first week of the master schedule record:
z - 1
ATPt = EIt - 1 + MPSt - AOBi (12.2)
i = t
For any subsequent week in which MPS 7 0:
|
z - 1 |
|
ATPt = MPSt - AOBi |
|
i = t |
where: |
= available to promise in week t |
ATPt |
|
EIt - 1 |
= ending inventory in week t – 1 |
MPSt |
= master production schedule quantity in week t |
z - 1 |
|
AOBi |
= sum of all orders booked from week t until week z (when the next positive MPS |
i = t |
quantity is due) |
Net requirements for the MRP record (page 370):
NRt = maximum 0; GRt - EIt - 1 - SRt
where:
NRt = net requirement in time period t
GRt = gross requirement in time period t
EIt - 1 = ending inventory from time period t – 1
SRt = scheduled receipts in time period t
Projected ending inventory for the MRP record (page 370):
EIt = EIt - 1 + SRt + PRt - GRt
where:
EIt = ending inventory from time period t
EIt - 1 = ending inventory from time period t – 1
SRt = scheduled receipts in time period t
PRt = planned receipts in time period t
GRt = gross requirements in time period t
Critical ratio (page 377):
days until due
Critical ratio = total task time remaining
(12.3)
(12.4)
(12.5)
(12.6)
Available to promise (ATP) 363 |
Forecasted demand |
Parent/child relationship 374 |
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Bill of material (BOM) 368 |
Job sequencing rules 376 |
Planning and control |
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Booked orders 362 |
Master production schedule (MPS) 362 |
Planning horizon |
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Dependent demand inventory 367 |
Master scheduling |
Planning lead time |
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Distribution requirements planning |
Material requirements planning |
Product structure tree |
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(DRP) 378 |
(MRP) 367 |
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Projected ending inventory | ||
Exploding the BOM 369 |
MRP nervousness |
Rough-cut capacity planning |
384 PART IV • Planning and Controlling Operations and Supply Chains
Solved Problem
P r o b l e m Completing a Master Schedule Record
Complete the projected ending inventory and available to promise calculations for the follow-ing master schedule record. Interpret the results.
On-hand inventory at end of week 15: 222 |
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Week |
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16 |
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17 |
18 |
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19 |
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20 |
21 |
22 |
23 |
Forecasted demand |
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220 |
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220 |
215 |
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215 |
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210 |
210 |
205 |
205 |
Booked orders |
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192 |
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189 |
233 |
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96 |
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135 |
67 |
85 |
40 |
Projected ending inventory |
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Master production schedule |
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450 |
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430 |
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415 |
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400 |
Available to promise |
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Solution |
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The projected ending inventory values can be found using Equation (12.1): |
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EIt |
= EIt - 1 + MPSt |
- maximum |
Ft, OBt |
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(12.1) |
For weeks 16 through 18, the projected ending inventories are:
EI16 |
= 222 |
+ |
0 |
- maximum |
220, 192 |
= 2 |
EI17 |
= 2 + 450 |
- maximum |
220, 189 |
= 232 |
||
EI18 |
= 232 |
+ |
0 |
- maximum |
215, 233 |
= -1 |
Weeks 19 through 23 are calculated in a similar manner. In the projected ending inventory in week 18, the negative value suggests that there is not enough inventory to meet the forecasted demand. But has the company overpromised yet? To see, we need to calculate the ATP numbers:
ATP16 |
= 222 |
+ 0 - 192 = 30 |
|||
ATP17 |
= 450 |
- |
189 + 233 |
= 28 |
|
ATP19 |
= 430 |
- |
96 |
+ 135 |
= 199 |
ATP21 |
= 415 |
- |
67 |
+ 85 = 263 |
ATP23 = 400 - 40 = 360
The completed master schedule record is as follows:
On-hand inventory at end of week 15: 222 |
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Week |
16 |
17 |
18 |
19 |
20 |
21 |
22 |
23 |
Forecasted demand |
220 |
220 |
215 |
215 |
210 |
210 |
205 |
205 |
Booked orders |
192 |
189 |
233 |
96 |
135 |
67 |
85 |
40 |
Projected ending inventory |
2 |
232 |
–1 |
214 |
4 |
209 |
4 |
199 |
Master production schedule |
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450 |
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430 |
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415 |
|
400 |
Available to promise |
30 |
28 |
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199 |
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263 |
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360 |
The master schedule record suggests that, as of right now, the company has not overpromised. However, if the company wants to meet all the forecasted demand (or keep some safety stock available, just in case), it should consider taking steps to bump up its master production sched-ule quantities.
1. Someone says to you, “If a company is already using sales and operations planning to coordinate marketing and operations, then it doesn’t need master scheduling as well.” Is this true? How are S&OP and master scheduling similar? How are they different? What information does master scheduling provide that S&OP does not? How difficult would it be to develop successful master schedules without doing S&OP first?
2. Can a company complete its material requirements plans before it does master scheduling? Explain.
3. Discuss the importance of accurate forecasting to plan-ning and control systems. What happens if an organiza-tion’s planning efforts are strong, except for forecasting?
4. What is MRP nervousness? Can this condition affect DRP systems as well?
CHAPTER 12 • Managing Production across the Supply Chain 385
5. Master scheduling, MRP, and DRP have been around for a long time, but too many companies still do an inadequate job of using these tools. How can this be? In particular, what role do you think organizational discipline plays in making these tools work?
6. What are the benefits of having a formal master schedul-ing process? What could happen to firms that don’t follow some of the basic rules of master scheduling?
7. Explain in your own words how tools such as master scheduling, MRP, and DRP can be used to coordinate activity up and down a supply chain. For example, what information might we share with our customers? Our sup-pliers? What information might we want from them to do master scheduling, MRP, and DRP effectively?
(* = easy; ** = moderate; *** = advanced)
Problems for Section 12.1: Master Scheduling
1. (*) Complete the following master schedule record:
On-hand inventory at end of week 1: 65 |
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Week |
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2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
Forecasted demand |
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45 |
50 |
55 |
60 |
65 |
70 |
75 |
80 |
Booked orders |
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15 |
100 |
48 |
25 |
72 |
22 |
67 |
10 |
Projected ending inventory |
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Master production schedule |
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150 |
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200 |
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150 |
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Available to promise |
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73 |
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2. (**) Consider the master schedule record shown in Problem 1. Suppose marketing books an order for an additional 10 units in week 4. Recalculate the projected ending inventory and avail-able-to-promise numbers. How low does the projected ending inventory get? What actions might the company take as a result?
3. (**) Complete the following master schedule record:
On-hand inventory at end of week 1: 100 |
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Week |
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2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
Forecasted demand |
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250 |
250 |
300 |
300 |
350 |
350 |
250 |
250 |
Booked orders |
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265 |
255 |
270 |
245 |
260 |
235 |
180 |
100 |
Projected ending inventory |
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Master production schedule |
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500 |
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600 |
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700 |
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500 |
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Available to promise |
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220 |
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4. (**) Consider the master schedule record shown in Problem 3. Suppose the production manager calls and says that only 600 units will be finished in week 6, not the 700 units originally called for. Recalculate the projected ending inventory and available-to-promise numbers. What does a negative projected ending inventory value mean? How does it differ from a negative available-to-promise number? As a manager, which would be easier to deal with—a negative projected inven-tory value or a negative ATP?
5. Consider the following partially completed master schedule record:
On-hand inventory at end of April: 40 |
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Month |
************May************ |
************June************ |
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Week |
19 |
20 |
21 |
22 |
23 |
24 |
25 |
26 |
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Forecasted demand |
200 |
200 |
200 |
225 |
225 |
225 |
200 |
200 |
Booked orders |
205 |
203 |
201 |
195 |
193 |
190 |
182 |
178 |
Projected ending inventory |
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Master production schedule |
600 |
0 |
0 |
675 |
0 |
0 |
600 |
0 |
Available to promise |
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240 |
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a. (*) Complete the projected on-hand inventory calculations and the available-to-promise calculations.
b. (**) Suppose that a customer calls and cancels an order for 50 units in week 25. Which of the following statements are true?
· The ATP for week 25 will increase by 50 units.
· The projected ending inventory for week 25 will increase by 50 units.
· The ATP for weeks 19 and 22 will be unaffected.
386 PART IV • Planning and Controlling Operations and Supply Chains
Problems for Section 12.2: Material Requirements Planning
6. (*) Complete the following MRP record. All gross requirements, beginning inventory levels, and scheduled receipts are shown.
Week |
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1 |
2 |
3 |
4 |
5 |
6 |
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***A2*** |
Gross requirements |
200 |
200 |
200 |
300 |
300 |
300 |
LT (weeks) = 2 |
Scheduled receipts |
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200 |
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Projected ending inventory: 260 |
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Net requirements |
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Min. order = 1 |
Planned receipts |
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Planned orders |
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7. (**) Now suppose the lead time for item A2, shown in Problem 6, is three weeks rather than two weeks. Based on this information, can the company support the current gross requirements for the A2? Why? What are the implications of having reliable supplier and manufacturing lead times in an MRP environment?
8. (**) Complete the following MRP record. Note that the minimum order quantity is 900. What is the average ending inventory over the six weeks?
Week |
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1 |
2 |
3 |
4 |
5 |
6 |
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***B3*** |
Gross requirements |
0 |
400 |
400 |
400 |
0 |
400 |
LT (weeks) = 1 |
Scheduled receipts |
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Projected ending inventory: 0 |
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Net requirements |
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Min. order = 900 |
Planned receipts |
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Planned orders |
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9. (**) Now suppose the minimum order quantity for item B3 in Problem 8 is reduced to 300 units. Redo the MRP record. What is the new average ending inventory level over the six weeks? What are the implications for setting order quantities in an MRP environment?
10. (**) The following figure shows the bill of material (BOM) for the Acme PolyBob, a product that has proven unsuccessful in capturing roadrunners. Complete the following MRP records for Components B, C, E, and F. All the information you need is shown in the BOM and on the MRP records.
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A |
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*2 required |
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B |
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C |
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E |
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F |
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D |
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E |
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F |
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*3 required |
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*2 required |
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Item B: Lead time = 1 week; Minimum order quantity = 1 |
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Week |
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4 |
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5 |
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6 |
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Gross requirements |
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250 |
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300 |
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300 |
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300 |
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200 |
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Scheduled receipts |
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Projected ending inventory: 0 |
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Net requirements |
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Planned receipts |
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Planned orders |
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CHAPTER 12 • Managing Production across the Supply Chain 387
Item C: Lead time = 3 weeks; Minimum order quantity = 500
Week |
1 |
2 |
3 |
4 |
5 |
6 |
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Gross requirements |
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Scheduled receipts |
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500 |
600 |
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Projected ending inventory: 0 |
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Net requirements |
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Planned receipts |
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Planned orders |
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Item E: Lead time = 4 weeks; Minimum order quantity = 5,000
Week |
1 |
2 |
3 |
4 |
5 |
6 |
Gross requirements
Scheduled receipts
Projected ending inventory: 5,750
Net requirements
Planned receipts
Planned orders
Item F: Lead time = 5 weeks; Minimum order quantity = 750
Week |
1 |
2 |
3 |
4 |
5 |
6 |
Gross requirements
Scheduled receipts
Projected ending inventory: 4,750
Net requirements
Planned receipts
Planned orders
11. (**) Republic Tool and Manufacturing Company of Carlsbad, California, makes a wide variety of lawn care products. One of Republic’s products is the Model Number 540 Broadcast Spreader:
Broadcast spreader kit
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Hopper |
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Frame |
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Cotter |
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Gear & rotor plate |
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assembly |
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pin |
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assembly |
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*2 needed |
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U-frame |
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Frame |
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Drive |
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Rotor |
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Cotter |
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legs |
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gear |
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plate |
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pin |
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*2 needed
Complete the following MRP records. Note the following:
· Republic intends to start assembling 2,000 broadcast spreader kits in weeks 2, 4, and 6.
· The gross requirements for the gear and rotor plate assembly have already been given to you. For the remaining items, you will need to figure out the gross requirements.
· All scheduled receipts, lead times, and beginning inventory levels are shown.
· Note that cotter pins appear twice in the bill of material.
Gear and rotor plate assembly: Lead time = 1 week; Minimum order quantity = 2,500
Week |
1 |
2 |
3 |
4 |
5 |
6 |
Gross requirements |
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2,000 |
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2,000 |
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2,000 |
Scheduled receipts |
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Projected ending inventory: 1,000 |
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Net requirements |
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Planned receipts |
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Planned orders |
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388 PART IV • Planning and Controlling Operations and Supply Chains
Wheels: Lead time = 1 week; Minimum order quantity = 1
Week |
1 |
2 |
3 |
4 |
5 |
6 |
Gross requirements
Scheduled receipts
Projected ending inventory: 0
Net requirements
Planned receipts
Planned orders
Cotter pins: Lead time = 3 weeks; Minimum order quantity = 15,000
Week |
1 |
2 |
3 |
4 |
5 |
6 |
Gross requirements
Scheduled receipts
Projected ending inventory: 11,000
Net requirements
Planned receipts
Planned orders
12. (**) Each Triam Deluxe gamer computer system consists of two speakers, a monitor, a system unit, a keyboard, and an installation kit. These pieces are packed together and shipped as a com-plete kit. In MRP terms, all of these items are level 1 items that make the level 0 kits. Complete the MRP records, using the following information:
· Production plans for complete kits are as follows: Start assembling 2,500 kits in week 2
Start assembling 3,000 kits in weeks 3, 4, and 5 Start assembling 2,000 kits in week 6
· The gross requirements for the system unit have already been given to you. For the remaining items, you will need to figure out the gross requirements.
· All scheduled receipts, lead times, and beginning inventory levels are shown.
System unit: Lead time = 1 week; Minimum order quantity = 1
Week |
1 |
2 |
3 |
4 |
5 |
6 |
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Gross requirements |
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2,500 |
3,000 |
3,000 |
3,000 |
2,000 |
Scheduled receipts |
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Projected ending inventory: 0 |
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Net requirements |
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Planned receipts |
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Planned orders |
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Speakers: Lead time = 1 week; Minimum order quantity = 5,000
Week |
1 |
2 |
3 |
4 |
5 |
6 |
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Gross requirements |
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Scheduled receipts |
5,000 |
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Projected ending inventory: 0 |
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Net requirements |
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Planned receipts |
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Planned orders |
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CHAPTER 12 • Managing Production across the Supply Chain 389
Keyboards: Lead time = 6 weeks; Minimum order quantity = 5,000
Week |
1 |
2 |
3 |
4 |
5 |
6 |
Gross requirements
Scheduled receipts
Projected ending inventory: 13,500
Net requirements
Planned receipts
Planned orders
Vaxidene (Problems 13 and 14)
After graduating from college, you take a job with Baxter Pharmaceuticals. You are made the product manager for Vaxidene, a new vaccine used to fight bacterial meningitis. The bill of mate-rial (BOM) for a single 4-milligram dose of Vaxidene follows:
Each dose is actually a mixture of three proprietary compounds, called compounds X, Y, and Z. It takes one week to mix them together to make doses of Vaxidene. You also have the following information:
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Vaxidene (4 mg) |
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Compound |
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Compound |
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Compound |
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X (2 mg) |
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Y (1 mg) |
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Z (1 mg) |
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Chemical A |
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Chemical B |
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Chemical A |
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Chemical C |
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Chemical C |
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Chemical D |
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(1 mg) |
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(1 mg) |
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(0.5 mg) |
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(0.5 mg) |
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(0.5 mg) |
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(0.5 mg) |
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· Compound X is made up of two chemicals (A and B) and takes one week to synthesize (i.e., lead time = 1 week).
· Compound Y is made up of two chemicals (A and C) and takes one week to synthesize.
· Compound Z is made up of two chemicals (C and D) and takes one week to synthesize.
· The lead times for chemicals A through D are all one week.
13. Consider the following master schedule record for Vaxidene:
On-hand inventory at end of December: 916 doses |
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||
Month |
**********January********** |
********February******** |
||||||
Week |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
Forecast demand |
1,000 |
1,000 |
1,250 |
1,250 |
1,500 |
1,500 |
1,750 |
1,750 |
Orders booked |
1,095 |
950 |
1,100 |
963 |
1,125 |
1,095 |
1,243 |
1,208 |
Projected on-hand |
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inventory |
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Master schedule |
3,250 |
0 |
0 |
4,250 |
0 |
0 |
3,500 |
0 |
Available-to-promise |
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a. (*) Complete the master schedule record.
b. (**) Suppose a hospital in the Tucson area calls and says it is facing an epidemic of bacterial meningitis. It needs 2,000 doses as soon as possible. Assuming that Baxter can make no changes to the master production schedule quantities or orders booked, how quickly can it get the hospi-tal the 2,000 doses? Be specific with regard to what quantities Baxter can ship and when.
c. (**) Suppose the hospital says it needs the doses now, not in three weeks. What steps could Baxter Pharmaceuticals take to deal with this emergency? Who would Baxter need to talk to? (Hint: Consider the current booked orders and their sources.)
14. (***) Complete the following MRP records for the Vaxidene drug. Note the following:
· Doses have been converted into milligrams to facilitate material planning (4,250 doses = 17,000 milligrams).
· Make sure that you calculate the correct requirements for each compound and drug. For instance, each 4-milligram dose requires 2 milligrams of compound X (2 to 1). Therefore, to start mixing 17,000 milligrams of Vaxidene, Baxter Pharmaceuticals will need 8,500 milligrams of compound X.
390 PART IV • Planning and Controlling Operations and Supply Chains
***Vaxidene***
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Week |
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
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LT (weeks) = 1 start mixing |
Mps due date |
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0 |
0 |
17,000 |
0 |
0 |
14,000 |
|
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17,000 |
|
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14,000 |
|
***Compound X*** |
Gross requirements |
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LT (weeks) = |
Scheduled receipts |
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Projected ending inventory: 1,500 |
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Net requirements |
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Minimum order (mg) = 20,000 |
Planned receipts |
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***Compound Y*** |
Planned orders |
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Gross requirements |
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LT (weeks) = |
Scheduled receipts |
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Projected ending inventory: 1,000 |
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Net requirements |
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Minimum order (mg) = 5,000 |
Planned receipts |
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***Compound Z*** |
Planned orders |
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Gross requirements |
200 |
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LT (weeks) = |
Scheduled receipts |
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Projected ending inventory: 0 |
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Net requirements |
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Minimum order (mg) = 1 |
Planned receipts |
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***Chemical A*** |
Planned orders |
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Gross requirements |
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LT (weeks) = |
Scheduled receipts |
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Projected ending inventory: 500 |
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Net requirements |
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Minimum order (mg) = 9,500 |
Planned receipts |
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***Chemical B*** |
Planned orders |
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Gross requirements |
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LT (weeks) = |
Scheduled receipts |
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Projected ending inventory: 0 |
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Net requirements |
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Minimum order (mg) = 4,000 |
Planned receipts |
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***Chemical C*** |
Planned orders |
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Gross requirements |
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LT (weeks) = |
Scheduled receipts |
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Projected ending inventory: 0 |
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Net requirements |
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Minimum order (mg) = 2,000 |
Planned receipts |
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***Chemical D*** |
Planned orders |
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Gross requirements |
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LT (weeks) = |
Scheduled receipts |
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Projected ending inventory: 3,000 |
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Net requirements |
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Minimum order (mg) = 1 |
Planned receipts |
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Planned orders |
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CHAPTER 12 • Managing Production across the Supply Chain 391
Problems for Section 12.3: Production Activity Control and Vendor Order Management Systems
15. (**) Consider the following job information. Each job must proceed sequentially through the dif-ferent work areas, and each area can work on only one job at a time. Sequence the jobs according to the (1) first-come, first-served rule, (2) earliest due date, and (3) critical ratio. Calculate the average lateness under each rule. Which rule performs best? Are any of the results completely satisfactory? What are the implications?
|
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Estimated Days |
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Job |
Painting |
Assembly |
Packing |
Total Task Time |
Days Until Due |
A |
1.5 |
2 |
0.5 |
4 |
15 |
B |
4 |
3 |
1 |
8 |
16 |
C |
3 |
2 |
0.5 |
5.5 |
8 |
D |
6 |
4 |
1 |
11 |
20 |
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16. (**) Recall Example 12.7, where Carlos’s Restoration Services wanted to determine the best sequence for four jobs. According to Table 12.2, the critical ratio was the only rule tested that resulted in an estimated average lateness value of 0 days.
Now suppose a representative of Chester College calls and says the college won’t need the restored art piece for 14 days. At the same time, the High Museum leaves a message saying it would really like its job completed in 15 days.
How would these requests change the suggested sequence, based on the critical ratio rule? If Carlos’s uses the critical ratio rule to sequence the jobs, will they all be done on time?
Problems for Section 12.4: Synchronizing Planning and Control across the Supply Chain
17. (***) Due to unusual weather conditions in Minneapolis and Buffalo, Sandy-Built changed the fore-casted demand numbers for MeltoMatic snowblowers at its two distribution centers. Complete the following new DRP records and master schedule record.
Minneapolis Distribution Center
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Month |
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November |
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December |
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January |
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Week |
45 |
46 |
47 |
48 |
49 |
50 |
51 |
52 |
1 |
2 |
3 |
4 |
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Forecasted demand |
80 |
80 |
80 |
80 |
90 |
90 |
100 |
100 |
120 |
120 |
140 |
140 |
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LT (weeks) = 2 |
Scheduled receipts |
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Projected ending inventory: 160 |
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Net requirements |
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Minimum order = |
Planned receipts |
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120 |
Planned orders |
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Buffalo Distribution Center
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Month |
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November |
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December |
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January |
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Week |
45 |
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46 |
47 |
48 |
49 |
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50 |
51 |
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52 |
1 |
2 |
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3 |
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4 |
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Forecasted demand |
60 |
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60 |
70 |
70 |
80 |
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80 |
80 |
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80 |
90 |
90 |
|
95 |
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95 |
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LT (weeks) = 1 |
Scheduled receipts |
100 |
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Projected ending inventory: 25 |
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Net requirements |
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Minimum order = |
Planned receipts |
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100 |
Planned orders |
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Master Schedule |
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Month |
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November |
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December |
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Week |
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45 |
46 |
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47 |
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48 |
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49 |
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50 |
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51 |
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52 |
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Total planned orders |
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Booked orders |
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120 |
0 |
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0 |
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0 |
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0 |
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0 |
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Projected ending inventory: 37 |
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Master production schedule |
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340 |
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320 |
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340 |
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440 |
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Available to promise |
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392 PART IV • Planning and Controlling Operations and Supply Chains
The Realco Breadmaster
Two years ago, Johnny Chang’s company, Realco, introduced a new breadmaker, which, due to its competitive pricing and features, was a big success across the United States. While de-lighted to have the business, Johnny felt uneasy about the lack of formal planning surrounding the product. He found him-self constantly wondering, “Do we have enough breadmakers to meet the orders we’ve already accepted? Even if we do, will we have enough to meet expected future demands? Should I be doing something right now to plan for all this?”
To get a handle on the situation, Johnny decided to talk to various folks in the organization. He started with his inventory manager and found out that inventory at the end of last week was 7,000 units. Johnny thought this was awfully high.
Johnny also knew that production had been completing 40,000 breadmakers every other week for the last year. In fact, another batch was due this week. The production numbers were based on the assumption that demand was roughly 20,000 breadmakers a week. In over a year, no one had questioned whether the forecast or production levels should be readjusted.
Johnny then paid a visit on his marketing manager to see what current orders looked like. “No problem,” said Jack Jones, “I have the numbers right here.”
Week Promised Shipments
1 23,500
2 23,000
3 21,500
4 15,050
5 13,600
6 11,500
7 5,400
8 1,800
Johnny looked at the numbers for a moment and then asked, “When a customer calls up, how do you know if you can meet his order?” “Easy,” said Jack. “We’ve found from experi-ence that nearly all orders can be filled within two weeks, so we promise them three weeks. That gives us a cushion, just in case. Now look at weeks 1 and 2. The numbers look a little high, but between inventory and the additional 40,000 units coming in this week, there shouldn’t be a problem.”
Questions
1. Develop a master production schedule for the breadmaker. What do the projected ending inventory and available-to-promise numbers look like? Has Realco overpromised? In your view, should Realco update either the forecast or the production numbers?
2. Comment on Jack’s approach to order promising. What are the advantages? The disadvantages? How would for-mal master scheduling improve this process? What orga-nizational changes would be required?
3. Following up on Question 2, which do you think is worse: refusing a customer’s order up front because you don’t have the units available or accepting the order and then failing to deliver? What are the implications for master scheduling?
4. Suppose Realco produces 20,000 breadmakers every week rather than 40,000 every other week. According to the master schedule record, what impact would this have on average inventory levels?
Books and Articles Bundy, W. “Miller SQA: Leveraging Technology for Speed and
Blackstone, J. H., ed., APICS Dictionary, 14th ed. (Chicago, IL: Reliability,” Supply Chain Management Review 3, no. 2
APICS, 2013). (Spring 1999): 62–69.
Supplement
Paul A. Souders/Corbis
Supplement Outline
Introduction
12S.1 Understanding Supply Chain Information Needs
12S.2 Supply Chain Information Systems
12S.3 Trends to Watch Supplement Summary
Supplement ObjectiveS
By the end of this supplement, you will be able to:
· Explain why information flows are a necessary part of any supply chain and describe in detail how supply chain information needs vary according to the organizational level and the direction of the linkages (upstream or downstream).
· Describe and differentiate among ERP, DSS, CRM, SRM, and logistics applications.
· Describe what business process management (BPM) tools and cloud computing are and how they might impact future operations and supply chain activities.
393
394 PART IV • Planning and Controlling Operations and Supply Chains
Information system (IS)
According to Laudon and Laudon, “A set of interrelated components that collect
(or retrieve), process, store, and distribute information to support decision making, coordination, and control in an organization.”
Whether we are talking about purchasing or forecasting, master scheduling or project planning, information is an essential part of managing operations and supply chains. Imagine, for example, trying to decide how much capacity your organization needs or how much of a product to make if you don’t have a clear idea of what the demand will be or what the relevant costs are.
The importance of information is reflected in the APICS definition of supply chain: “The global network used to deliver products and services from raw materials to end customers through an engineered flow of information, physical distribution, and cash.”1 In fact, one could argue that neither physical nor monetary flows could take place without information flows.
In this supplement, we look at supply chain information flows and the types of informa-tion systems firms use to carry them out. Laudon and Laudon define an information system (IS) as “a set of interrelated components that collect (or retrieve), process, store, and dis-tribute information to support decision making, coordination, and control in an organiza-tion.”2 We should note that not all information systems are computer-based. Nevertheless, much of the growth and interest in supply chain information systems lies in computer-based applications.
This supplement is divided into two parts. In the first part, we discuss the critical role information flows play in the supply chain. Our purpose here is to give you an understanding of the different ways in which information is used. The second section shifts the focus away from information flows to information systems . In particular, we discuss some of the major categories of supply chain information systems, including enterprise resource planning (ERP) systems.
12S.1 Understanding Supply Chain Information Needs
Companies use information to help do everything from handling customers’ orders to develop-ing new business strategies. It makes sense, then, to start our discussion of supply chain informa-tion flows by describing the different ways in which information supports supply chain activities. Common sense tells us that if we understand what the information needs are, we will be in a better position to identify possible solutions later on.
Differences across Organizational Levels
Some of the supply chain activities we have described in this book are particularly information intensive. These include:
1. Execution and transaction processing (e.g., vendor order management systems);
2. Routine decision making (e.g., master scheduling and supplier evaluation systems);
3. Tactical planning (e.g., S&OP); and
4. Strategic decision making (e.g., location modeling, qualitative forecasting, capacity decisions).
Table 12S.1 arranges these categories vertically, with longer- term strategic decision making at the top and day-to-day, routine activities at the bottom. By looking at supply chain activities in this way, we can begin to see how supply chain information needs differ at various levels of the organization.
At the lowest levels, supply chain information flows record and retrieve necessary data and execute and control physical and monetary flows. This is referred to as execution and transac-tion processing. Information flows at this level tend to be highly automated, with a great deal of
1Definition of Supply Chain in J. H. Blackstone, ed., APICS Dictionary, 14th ed. (Chicago, IL: APICS, 2013). Reprinted by permission.
2Laudon, K., and Laudon, J., Management Information Systems: Managing the Digital Firm, 13th ed. (Upper Saddle River,
NJ: Prentice Hall, 2013).
CHAPTER 12S • Supply Chain Information Systems 395
Table 12S.1 Supply Chain Information Needs
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Key Performance |
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Supply Chain |
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Dimensions for |
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ACtivity |
Purpose |
Characteristics |
Information Flows |
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Strategic decision |
Develop long-range |
• |
Focus is on long-term decisions, such |
• |
Flexibility |
making |
strategic plans for |
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as new products or markets and |
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meeting the |
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brick-and-mortar capacity decisions |
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organization’s mission |
• Least structured of all; |
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information needs can change |
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dramatically from one effort |
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to the next |
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Greatest user discretion |
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Tactical planning |
Develop plans that |
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Focus is on tactical decisions, |
• |
Form |
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coordinate the actions |
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such as inventory or work force levels |
• |
Flexibility |
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of key supply chain |
• Plans, but does not carry out, physical |
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areas, customers, and |
• |
flows |
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suppliers across the |
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Greater user discretion |
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tactical time horizon |
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Routine decision |
Support rule-based |
• |
Fairly short time frames |
• |
Accuracy |
making |
decision making |
• |
User discretion |
• |
Timeliness |
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• |
Limited flexibility |
Execution and |
Record and retrieve |
• |
Very short time frames, very |
• |
Accuracy |
transaction |
data and execute |
• |
high volumes |
• |
Timeliness |
processing |
and control physical |
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Highly automated |
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and monetary flows |
• |
Standardized business practices |
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• Ideally no user intervention |
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emphasis on performing the activity the same way each time. The best execution and transaction processing flows require little or no user intervention and are very accurate and fast.
At a somewhat higher level, information flows are used to support routine decision mak-ing. Here, users often must have some flexibility to handle exceptions. For example, a retailer might use an inventory management system to forecast, calculate order quantities, establish re-order points, and release orders for the vast majority of items. But the retailer may still want the ability to override the software when the situation warrants.
The next level up is tactical planning. Here managers are responsible for developing plans that coordinate the actions of key supply chain areas, customers, and suppliers across some tacti-cal time horizon, usually a few months to a year out. Information requirements at this level dif-fer from those at lower levels in a number of ways. First, the information must support planning activities not actual execution. Therefore, the time frames are somewhat longer and accuracy is important, but not to the same degree as at lower levels. Second, the information must be widely available and in a form that can be interpreted, manipulated, and used by parties with very different perspectives. A classic example is sales and operations planning (S&OP), which we described in Chapter 10.
Finally, information is needed to support strategic decision making. Here sophisticated analytical tools are often used to search for patterns or relationships in data. Examples include customer segment analysis, product life cycle forecasting, and what-if analyses regarding long-term product or capacity decisions. An excellent example of this is the simulation model we developed for Luc’s Deluxe Car Wash in the Chapter 6 supplement. Information systems at the strategic level must be highly flexible in how they manipulate and present the data because the strategic question of interest may change from one situation to the next. Later in this chapter, we talk about decision support systems (DSS), which are specifically geared to support strategic decision making. Notice how the name emphasizes the fact that these systems support, but do not make, decisions for top managers.
396 PART IV • Planning and Controlling Operations and Supply Chains
Customer relationship management (CRM)
A term that broadly refers to planning and control activities and information systems that link a firm with its downstream customers.
Supplier relationship management (SRM)
A term that broadly refers to planning and control activities and information systems that link a firm with its upstream suppliers.
Internal supply chain management
A term that refers to the infor-mation flows between higher and lower levels of planning and control systems within an organization.
Customer |
Internal Supply |
Supplier |
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Relationship |
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Relationship |
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Management |
Chain Management |
Management |
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Strategic decision making |
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Tactical planning |
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Routine decision making |
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Execution and transaction |
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processing |
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Figure 12S.1 Supply Chain Information Flows
In addition to the organizational level, we need to consider the direction of the linkages. For ex-ample, there are planning and control activities that link a firm with its downstream customers, broadly referred to as customer relationship management (CRM) activities, and those that link a firm with its upstream suppliers, known as supplier relationship management (SRM) activi-ties (Figure 12S.1). There are also flows that link higher-level planning and decision making with lower-level activities within the firm (dubbed internal supply chain management by Chopra and Meindl3).
12S.2 Supply Chain Information Systems
In this section, we shift our focus from a general discussion of supply chain information flows to a description of the different solutions currently being offered. The basis of our map was first laid out more than a decade ago by Steven Kahl,4 then a software industry analyst. Kahl’s map was later refined by Chopra and Meindl,5 who applied the labels CRM, SRM, and internal supply chain management (ISCM) to various areas of the map.
Our map (Figure 12S.2) parallels Figure 12S.1 in that it distinguishes the various applica-tions by organizational level and the direction of linkages. We add an additional column labeled
Strategic |
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decision |
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DSS |
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Network |
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making |
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Tactical |
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SRM |
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CRM |
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design |
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applications |
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applications |
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planning |
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Warehouse and |
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Routine |
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ERP |
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transportation |
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decision |
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planning |
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making |
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applications |
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Warehouse |
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Execution |
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management |
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and transportation |
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and |
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execution |
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transaction |
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processing |
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Suppliers |
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Internal supply |
Customers |
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Logistics |
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chain
Figure 12S.2 A Map of Supply Chain Management Information Systems
3S. Chopra and P. Meindl, Supply Chain Management: Strategy, Planning and Operation, 5th ed. (Upper Saddle River, NJ: Prentice Hall, 2012).
4S. Kahl, “What’s the ‘Value’ of Supply Chain Software?” Supply Chain Management Review 2, no. 4 (Winter 1999): 59–67.
5Chopra and Meindl, Supply Chain Management.
CHAPTER 12S • Supply Chain Information Systems 397
Figure 12S.3 |
Applications with a common technological platform and built-in integration ... |
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ERP Systems |
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. . .and others |
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Finance |
Accounting |
Marketing |
Sales |
Operations |
Purchasing |
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. . . can share a common
Centralized set of data database
Enterprise resource planning (ERP) systems
Large, integrated, computer-based business transaction processing and reporting systems. ERP systems pull together all of the classic business functions such as accounting, finance, sales, and operations into a single, tightly integrated package that uses a common database.
Decision support systems (DSS)
Computer-based information systems that allow users to analyze, manipulate, and pres-ent data in a manner that aids higher-level decision making.
“Logistics.” Logistics applications deal with facilities and transportation issues, such as deter-mining facility locations, optimizing transportation systems, and controlling the movement of materials between supply chain partners.
Enterprise resource planning (ERP) systems are large, integrated, computer-based business transaction processing and reporting systems. The primary advantage of ERP sys-tems is that they pull together all of the classic business functions, such as accounting, finance, sales, and operations, into a single, tightly integrated package that uses a common database (Figure 12S.3).
To understand why this is such a big deal, imagine what the information systems for a typical company looked like before ERP. First, every functional area had its own set of soft-ware applications, often running on completely different systems. Sharing information (e.g., forecasts, customer information) between systems was a nightmare. To make matters worse, the same information often had to be entered multiple times in different ways. ERP pulled all of these disparate systems into a single integrated system. In practice, few companies use ERP systems to serve all of their information requirements. Rather, companies use ERP systems to meet the bulk of their needs and “plug in” preexisting legacy systems and best-in -class applica-tions to tailor the system to their exact needs. Figure 12S.4 illustrates the idea. As you can imag-ine, making ERP systems integrate with other applications presents a significant technological challenge.
ERP’s traditional strengths lie in routine decision making and in execution and transac-tion processing. To the extent that ERP systems support higher-level planning and decision making, the focus is on the internal supply chain. ERP systems also capture much of the raw data needed to support higher-level decision support systems. Decision support systems (DSS) are computer-based information systems that allow users to analyze, manipulate, and present data in a manner that aids higher-level decision making.
SRM and CRM applications, in contrast, are computer-based information systems specifi-cally designed to help plan and manage the firm’s external linkages with its suppliers and cus-tomers, respectively. Table 12S.2 gives examples of the types of functionality provided by SRM and CRM applications.
Vendors specializing in CRM and SRM applications tend to provide greater functional-ity in their chosen areas than do ERP vendors. As a result, many firms choose a standard ERP package for routine decision making and for execution and transaction processing and use
Figure 12S.4 |
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Integrating ERP Systems |
ERP |
ERP |
ERP |
ERP |
Best in |
with Legacy and Best- |
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class |
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In-Class Applications |
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ERP |
ERP |
Legacy |
ERP |
Best in |
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class |
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ERP Legacy ERP ERP ERP
398 PART IV • Planning and Controlling Operations and Supply Chains
Table 12S.2 Examples of SRM and CRM Applications
SRM Applications |
CRM Applications |
Design collaboration |
Market analysis |
Sourcing decisions |
Sell process |
Negotiations |
Order management |
Buy process |
Call/service center management |
Supply collaboration |
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|
Source: Chopra, Sunil; Meindl, Peter, Supply Chain Management, 5th Ed., © 2013. Reprinted and Electronically reproduced by permission of Pearson Education, Inc., Upper Saddle River, New Jersey.
Network design applications
Logistics information systems that address such long-term strategic questions as facility location and sizing, as well as transportation networks. These applications often make use of simulation and optimization modeling.
Warehouse and transporta-tion planning systems
Logistics information systems that support tactical planning efforts by allocating “fixed” logistics capacity in the best possible way, given business requirements.
Warehouse management and transportation execution systems
Logistics information systems that initiate and control
the movement of materials between supply chain partners.
best-in-class CRM and SRM applications to manage external relationships. However, this situ-ation has changed in recent years, as ERP vendors, such as SAP and Oracle, increase the CRM and SRM functionality of their own systems.
The last set of supply chain IS applications we will discuss deals directly with logistics deci-sions. These applications can be divided into three main categories: network design applications, warehouse and transportation planning systems, and warehouse management and transporta-tion execution systems. Network design applications address such long-term strategic ques-tions as facility location and sizing, as well as transportation networks. These applications often make use of simulation and optimization modeling.
Warehouse and transportation planning systems support tactical planning efforts by al-locating “fixed” logistics capacity in the best possible way, given business requirements. These IS applications can also use optimization modeling and simulation. The warehouse assignment problem in Chapter 8, where we had to decide how many units to ship from each warehouse to each demand point, is a classic example of a warehouse and transportation planning system. To find the optimal answer, we built an optimization model that used data on fixed warehouse ca-pacities, demand levels, and shipping costs to generate the lowest-cost solution.
Finally, warehouse management and transportation execution systems initiate and control the movement of materials between supply chain partners. Within a warehouse, for example, sophisticated execution systems tell workers where to store items, where to go to pick them up, and how many to pick. Similarly, bar -code systems and global positioning systems (GPSs) have dramatically changed the ability of businesses to manage actual movements in the distribution system. Not too long ago, the only thing most transportation firms could tell you was that your shipment was “on the way” and “should be there in a day or two.” Now carriers can tell their customers the exact location of a shipment and the arrival time within minutes.
Of course, operations and supply chain information systems continue to evolve. Two trends of particular interest to operations and supply chain professionals are (1) the emergence of sophisti-cated business process management (BPM) tools and (2) cloud computing.
Business process modeling tools
According to Harmon, “Software tools that aid business teams in the analysis, modeling, and redesign of business processes.”
As we noted earlier, there may be times when a prepackaged software solution does not meet an organization’s needs. This is especially true when an organization wants to implement its own unique business processes. In his book Business Process Change: A Business Process Management Guide for Managers and Process Professionals, Harmon describes a number of software tools aimed at business process analysis and design. He highlights two key tools:
· Business process modeling tools are “software tools that aid business teams in the analysis, modeling, and redesign of business processes.”6 BP modeling tools do more
6Harmon, P., Business Process Change: A Business Process Management Guide for Managers and Process Professionals,
3rd ed. (Waltham, MA: Morgan Kaumann Publishers, 2014), p. 382.
Business process manage-ment systems (BPMS) products
According to Harmon, “Software tools that allow analysts to model processes and…then automate the execution of the process at run time.”
CHAPTER 12S • Supply Chain Information Systems 399
than just chart work flows; they allow users to graphically define a process and simulate the performance of the new process to gain insights into how it might work in the real world. BP modeling tools can also help users develop cost estimates based on the sequence of activities in a process and save defined processes in a database so that they can be reused again in other parts of the business.
· Business process management systems (BPMS) products are state-of-the-art software tools for developing and implementing business processes. As Harmon puts it, BPMS products are “software tools that allow analysts to model processes and… then auto-mate the execution of the process at run time.”7 Imagine how this would work: Experts use a BPMS product to develop a process map of how they want a process to work. They then define business rules to manage the flow of work through process (e.g., “If an order is scheduled to be finished late by X or more days, initiate the defined expedited ship-ping process”). When the users are satisfied that the new process works the way they want it to, the BPMS product can be used to automatically carry out future business activity, without requiring developers to write new software code.
Cloud computing
According to a National Institute of Standards and Technology (NIST) report “a model for enabling ubiquitous, convenient, on-demand net-work access to a shared pool of configurable computing resources (e.g., networks, serv-ers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.”
Prior to the Internet, the vast majority of computer systems were isolated from one another, requiring their own hardware, software, and databases. Even when organizations put in place private networks to link these systems, sharing software applications and data was a relatively difficult task. The Internet has changed all that and has led to the advent of what is broadly called cloud computing. Peter Mell and Timothy Grance of the National Institute of Standards and Technology (NIST) define cloud computing as “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.”8 According to Mell and Grance, the cloud model has five essential characteristics:
1. On-demand self-service. Users can automatically access applications and storage space whenever they need them.
2. Broad network access. Capabilities are available over the network and accessed through standard mechanisms that promote use by a wide range of platforms, including mobile phones, laptops, and PDAs.
3. Resource pooling. The provider’s computing resources are pooled to serve multiple consumers, with different physical and virtual resources dynamically assigned and reas-signed according to consumer demand.
4. Rapid elasticity. Capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out, and they can be rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
5. Measured service. Resource usage can be monitored, controlled, and reported, provid-ing transparency for both the provider and consumer of the utilized service.
So what does cloud computing mean for operations and supply chain management? Con-sider two quick examples. First, cloud computing makes it much easier for firms to outsource key portions of their business process information flows (e.g., credit checking, satellite tracking) to outside firms. Second, broad network access allows individual or computer systems to upload and retrieve information through a wide range of devices and virtually anywhere. In a nutshell, cloud computing will make supply chain information flows faster, more flexible, and cheaper than ever. How organizations take advantage of these breakthroughs to improve their existing operations or provide new products and services remains to be seen.
7Ibid., p. 384.
8P. Mell and T. Grance, The NIST Definition of Cloud Computing (Draft): Recommendations of the National Institute of Standards and Technology, NIST Special Publication 800-145, http://csrc.nist.gov/publications/nistpubs/800-145/ SP800-145.pdf.
400 PART IV • Planning and Controlling Operations and Supply Chains
In this supplement, we discussed the critical role information flows play in the supply chain and laid out a map of supply chain information systems. To conclude, we will consider the various ways in which information adds value and how break-throughs in technology will affect supply chain management activities over time. Just as the Internet was becoming popular, Jeffrey Rayport and John Sviokla wrote an article in which they talked about three ways information adds value.9 These ways were, in order of increasing value added:
1. Visibility;
2. Mirroring; and
3. Creation of new customer relationships.
Visibility represents the most basic function of informa-tion in the supply chain. Here information allows managers to “see” the physical and monetary flows in the supply chain and, as a result, better manage them. Classic examples include fore-casts and point-of-sales data, as well as information regard-ing inventory levels and the status of jobs in the production system.
Mirroring takes visibility a step further and seeks to replace certain physical processes with virtual ones. For example, Ray-port and Sviokla describe Boeing’s efforts to design new engine housings. In the past, Boeing had to create physical mock-ups of the housings and test them in a wind tunnel in order to evaluate their performance. This was a time-consuming and expensive process. But with the advent of powerful computers, Boeing was able to replace this physical process altogether:
Boeing engineers developed the prototype as a virtual prod-uct that incorporated relevant laws of physical and mate-rial sciences and enabled the company to test an evolving
computer-simulated model in a virtual wind tunnel. As a result, engineers could test many more designs at dramati-cally lower costs and with much greater speed.10
The third stage, creation of new customer relationships, involves taking raw information and organizing, selecting, synthesizing, and distributing it in a manner that creates whole new sources of value. Creating virtual, customized textbooks with hotlinks to instructor tutorials is one example. Other examples include taking raw supply chain data and turning them into graphical executive “dashboards” that allow managers to see, at a glance, how the overall business is performing.
So how has all this played out? Visibility systems continue to improve and provide more real-time data, especially as more organizations take advantage of cloud computing. In fact, many managers find themselves making decisions more often to take advantage of the increased availability of timely information. Second, more mirroring is occurring as many physical flows are replaced with virtual ones. Consider the case of Netflix, which started out managing the physical distribution of DVDs to cus-tomers but has now shifted to a business model based entirely on disseminating content via the Internet. Of course mirroring will be limited to those physical flows whose mission is to cre-ate or disseminate information (such as DVDs in the mail). It is highly unlikely that physical goods will be transformed and moved over the electronic superhighway anytime soon!
Finally, we can expect to see more information-based products aimed at the creation of new customer relationships. Because raw data can be used repeatedly and the variable costs of rearranging and organizing information are so low, this area is limited only by the imagination and needs of businesses.
Business process management systems (BPMS) products 399
Business process modeling tools 398 Cloud computing 399
Customer relationship management (CRM) 396
Decision support systems (DSS) | |
Enterprise resource planning (ERP) |
|
systems 397 |
|
Information system (IS) 394 |
|
Internal supply chain management |
Network design applications 398
Supplier relationship management (SRM) 396
Warehouse and transportation planning systems 398
Warehouse management and trans-portation execution systems 398
1. What is the difference between an information flow and an information system? Do information systems always have to be computerized? Why?
2. Consider Figure 12S.1. Some people have argued that companies need to put in place information systems that address routine decision making and transactional re-quirements prior to tackling higher-level planning and de-cision making. Others strongly disagree, pointing out that
the higher-level functions are a prerequisite to good tacti-cal planning and execution. What do you think?
3. SAP, the world leader in ERP systems software, has devel-oped tailored ERP systems for different industries. Go to www.sap.com/solution.html and examine the solutions for (1) a service industry and (2) a manufacturing indus-try of your choice. How are they similar? How are they different?
9J. Rayport and J. Sviokla, “Exploiting the Virtual Value Chain,” Harvard Business Review 73, no. 6 (November–December 1995): 75–85.
10Ibid., p. 79.
CHAPTER 12S • Supply Chain Information Systems 401
Books and Articles
Blackstone, J. H., ed., APICS Dictionary, 14th ed. (Chicago IL: APICS, 2013).
Chopra, S., and Meindl, P., Supply Chain Management: Strategy, Planning, and Operation, 5th ed. (Upper Saddle River, NJ: Prentice Hall, 2012).
Harmon, P., Business Process Change: A Business Process Management Guide for Managers and Process Profession-als, 3rd ed. (Waltham, MA: Morgan Kaufmann Publishers, 2014).
Kahl, S., “What’s the ‘Value’ of Supply Chain Software?” Sup-ply Chain Management Review 2, no. 4 (Winter 1999): 59–67.
Laudon, K., and Laudon, J., Management Information Systems: Managing the Digital Firm, 13th ed. (Upper Saddle River, NJ: Prentice Hall, 2013).
Rayport, J., and Sviokla, J., “Exploiting the Virtual Value Chain,” Harvard Business Review 73, no. 6 (November–December 1995): 75–85.
Internet
Mell, P., and Grance, T., The NIST Definition of Cloud Com-puting (Draft): Recommendations of the National Institute of Standards and Technology, NIST Special Publication 800-145, http://csrc.nist.gov/publications/nistpubs/ 800-145/SP800-145.pdf.